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SESA: Supervised Explicit Semantic Analysis | In recent years supervised representation learning has provided state of the
art or close to the state of the art results in semantic analysis tasks
including ranking and information retrieval. The core idea is to learn how to
embed items into a latent space such that they optimize a supervised objective
in that latent space. The dimensions of the latent space have no clear
semantics, and this reduces the interpretability of the system. For example, in
personalization models, it is hard to explain why a particular item is ranked
high for a given user profile. We propose a novel model of representation
learning called Supervised Explicit Semantic Analysis (SESA) that is trained in
a supervised fashion to embed items to a set of dimensions with explicit
semantics. The model learns to compare two objects by representing them in this
explicit space, where each dimension corresponds to a concept from a knowledge
base. This work extends Explicit Semantic Analysis (ESA) with a supervised
model for ranking problems. We apply this model to the task of Job-Profile
relevance in LinkedIn in which a set of skills defines our explicit dimensions
of the space. Every profile and job are encoded to this set of skills their
similarity is calculated in this space. We use RNNs to embed text input into
this space. In addition to interpretability, our model makes use of the
web-scale collaborative skills data that is provided by users for each LinkedIn
profile. Our model provides state of the art result while it remains
interpretable.
| 2,017 | Computation and Language |
Neural Machine Translation Leveraging Phrase-based Models in a Hybrid
Search | In this paper, we introduce a hybrid search for attention-based neural
machine translation (NMT). A target phrase learned with statistical MT models
extends a hypothesis in the NMT beam search when the attention of the NMT model
focuses on the source words translated by this phrase. Phrases added in this
way are scored with the NMT model, but also with SMT features including
phrase-level translation probabilities and a target language model.
Experimental results on German->English news domain and English->Russian
e-commerce domain translation tasks show that using phrase-based models in NMT
search improves MT quality by up to 2.3% BLEU absolute as compared to a strong
NMT baseline.
| 2,017 | Computation and Language |
Radical-level Ideograph Encoder for RNN-based Sentiment Analysis of
Chinese and Japanese | The character vocabulary can be very large in non-alphabetic languages such
as Chinese and Japanese, which makes neural network models huge to process such
languages. We explored a model for sentiment classification that takes the
embeddings of the radicals of the Chinese characters, i.e, hanzi of Chinese and
kanji of Japanese. Our model is composed of a CNN word feature encoder and a
bi-directional RNN document feature encoder. The results achieved are on par
with the character embedding-based models, and close to the state-of-the-art
word embedding-based models, with 90% smaller vocabulary, and at least 13% and
80% fewer parameters than the character embedding-based models and word
embedding-based models respectively. The results suggest that the radical
embedding-based approach is cost-effective for machine learning on Chinese and
Japanese.
| 2,017 | Computation and Language |
Making Sense of Word Embeddings | We present a simple yet effective approach for learning word sense
embeddings. In contrast to existing techniques, which either directly learn
sense representations from corpora or rely on sense inventories from lexical
resources, our approach can induce a sense inventory from existing word
embeddings via clustering of ego-networks of related words. An integrated WSD
mechanism enables labeling of words in context with learned sense vectors,
which gives rise to downstream applications. Experiments show that the
performance of our method is comparable to state-of-the-art unsupervised WSD
systems.
| 2,017 | Computation and Language |
N-gram and Neural Language Models for Discriminating Similar Languages | This paper describes our submission (named clac) to the 2016 Discriminating
Similar Languages (DSL) shared task. We participated in the closed Sub-task 1
(Set A) with two separate machine learning techniques. The first approach is a
character based Convolution Neural Network with a bidirectional long short term
memory (BiLSTM) layer (CLSTM), which achieved an accuracy of 78.45% with
minimal tuning. The second approach is a character-based n-gram model. This
last approach achieved an accuracy of 88.45% which is close to the accuracy of
89.38% achieved by the best submission, and allowed us to rank #7 overall.
| 2,016 | Computation and Language |
Argument Labeling of Explicit Discourse Relations using LSTM Neural
Networks | Argument labeling of explicit discourse relations is a challenging task. The
state of the art systems achieve slightly above 55% F-measure but require
hand-crafted features. In this paper, we propose a Long Short Term Memory
(LSTM) based model for argument labeling. We experimented with multiple
configurations of our model. Using the PDTB dataset, our best model achieved an
F1 measure of 23.05% without any feature engineering. This is significantly
higher than the 20.52% achieved by the state of the art RNN approach, but
significantly lower than the feature based state of the art systems. On the
other hand, because our approach learns only from the raw dataset, it is more
widely applicable to multiple textual genres and languages.
| 2,017 | Computation and Language |
What matters in a transferable neural network model for relation
classification in the biomedical domain? | Lack of sufficient labeled data often limits the applicability of advanced
machine learning algorithms to real life problems. However efficient use of
Transfer Learning (TL) has been shown to be very useful across domains. TL
utilizes valuable knowledge learned in one task (source task), where sufficient
data is available, to the task of interest (target task). In biomedical and
clinical domain, it is quite common that lack of sufficient training data do
not allow to fully exploit machine learning models. In this work, we present
two unified recurrent neural models leading to three transfer learning
frameworks for relation classification tasks. We systematically investigate
effectiveness of the proposed frameworks in transferring the knowledge under
multiple aspects related to source and target tasks, such as, similarity or
relatedness between source and target tasks, and size of training data for
source task. Our empirical results show that the proposed frameworks in general
improve the model performance, however these improvements do depend on aspects
related to source and target tasks. This dependence then finally determine the
choice of a particular TL framework.
| 2,017 | Computation and Language |
Unified Neural Architecture for Drug, Disease and Clinical Entity
Recognition | Most existing methods for biomedical entity recognition task rely on explicit
feature engineering where many features either are specific to a particular
task or depends on output of other existing NLP tools. Neural architectures
have been shown across various domains that efforts for explicit feature design
can be reduced. In this work we propose an unified framework using
bi-directional long short term memory network (BLSTM) for named entity
recognition (NER) tasks in biomedical and clinical domains. Three important
characteristics of the framework are as follows - (1) model learns contextual
as well as morphological features using two different BLSTM in hierarchy, (2)
model uses first order linear conditional random field (CRF) in its output
layer in cascade of BLSTM to infer label or tag sequence, (3) model does not
use any domain specific features or dictionary, i.e., in another words, same
set of features are used in the three NER tasks, namely, disease name
recognition (Disease NER), drug name recognition (Drug NER) and clinical entity
recognition (Clinical NER). We compare performance of the proposed model with
existing state-of-the-art models on the standard benchmark datasets of the
three tasks. We show empirically that the proposed framework outperforms all
existing models. Further our analysis of CRF layer and word-embedding obtained
using character based embedding show their importance.
| 2,017 | Computation and Language |
Break it Down for Me: A Study in Automated Lyric Annotation | Comprehending lyrics, as found in songs and poems, can pose a challenge to
human and machine readers alike. This motivates the need for systems that can
understand the ambiguity and jargon found in such creative texts, and provide
commentary to aid readers in reaching the correct interpretation. We introduce
the task of automated lyric annotation (ALA). Like text simplification, a goal
of ALA is to rephrase the original text in a more easily understandable manner.
However, in ALA the system must often include additional information to clarify
niche terminology and abstract concepts. To stimulate research on this task, we
release a large collection of crowdsourced annotations for song lyrics. We
analyze the performance of translation and retrieval models on this task,
measuring performance with both automated and human evaluation. We find that
each model captures a unique type of information important to the task.
| 2,017 | Computation and Language |
Automatic Identification of AltLexes using Monolingual Parallel Corpora | The automatic identification of discourse relations is still a challenging
task in natural language processing. Discourse connectives, such as "since" or
"but", are the most informative cues to identify explicit relations; however
discourse parsers typically use a closed inventory of such connectives. As a
result, discourse relations signaled by markers outside these inventories (i.e.
AltLexes) are not detected as effectively. In this paper, we propose a novel
method to leverage parallel corpora in text simplification and lexical
resources to automatically identify alternative lexicalizations that signal
discourse relation. When applied to the Simple Wikipedia and Newsela corpora
along with WordNet and the PPDB, the method allowed the automatic discovery of
91 AltLexes.
| 2,017 | Computation and Language |
Simple and Effective Dimensionality Reduction for Word Embeddings | Word embeddings have become the basic building blocks for several natural
language processing and information retrieval tasks. Pre-trained word
embeddings are used in several downstream applications as well as for
constructing representations for sentences, paragraphs and documents. Recently,
there has been an emphasis on further improving the pre-trained word vectors
through post-processing algorithms. One such area of improvement is the
dimensionality reduction of the word embeddings. Reducing the size of word
embeddings through dimensionality reduction can improve their utility in memory
constrained devices, benefiting several real-world applications. In this work,
we present a novel algorithm that effectively combines PCA based dimensionality
reduction with a recently proposed post-processing algorithm, to construct word
embeddings of lower dimensions. Empirical evaluations on 12 standard word
similarity benchmarks show that our algorithm reduces the embedding
dimensionality by 50%, while achieving similar or (more often) better
performance than the higher dimension embeddings.
| 2,017 | Computation and Language |
Emotion Intensities in Tweets | This paper examines the task of detecting intensity of emotion from text. We
create the first datasets of tweets annotated for anger, fear, joy, and sadness
intensities. We use a technique called best--worst scaling (BWS) that improves
annotation consistency and obtains reliable fine-grained scores. We show that
emotion-word hashtags often impact emotion intensity, usually conveying a more
intense emotion. Finally, we create a benchmark regression system and conduct
experiments to determine: which features are useful for detecting emotion
intensity, and, the extent to which two emotions are similar in terms of how
they manifest in language.
| 2,017 | Computation and Language |
Improved Abusive Comment Moderation with User Embeddings | Experimenting with a dataset of approximately 1.6M user comments from a Greek
news sports portal, we explore how a state of the art RNN-based moderation
method can be improved by adding user embeddings, user type embeddings, user
biases, or user type biases. We observe improvements in all cases, with user
embeddings leading to the biggest performance gains.
| 2,017 | Computation and Language |
WASSA-2017 Shared Task on Emotion Intensity | We present the first shared task on detecting the intensity of emotion felt
by the speaker of a tweet. We create the first datasets of tweets annotated for
anger, fear, joy, and sadness intensities using a technique called best--worst
scaling (BWS). We show that the annotations lead to reliable fine-grained
intensity scores (rankings of tweets by intensity). The data was partitioned
into training, development, and test sets for the competition. Twenty-two teams
participated in the shared task, with the best system obtaining a Pearson
correlation of 0.747 with the gold intensity scores. We summarize the machine
learning setups, resources, and tools used by the participating teams, with a
focus on the techniques and resources that are particularly useful for the
task. The emotion intensity dataset and the shared task are helping improve our
understanding of how we convey more or less intense emotions through language.
| 2,017 | Computation and Language |
Cross-Sentence N-ary Relation Extraction with Graph LSTMs | Past work in relation extraction has focused on binary relations in single
sentences. Recent NLP inroads in high-value domains have sparked interest in
the more general setting of extracting n-ary relations that span multiple
sentences. In this paper, we explore a general relation extraction framework
based on graph long short-term memory networks (graph LSTMs) that can be easily
extended to cross-sentence n-ary relation extraction. The graph formulation
provides a unified way of exploring different LSTM approaches and incorporating
various intra-sentential and inter-sentential dependencies, such as sequential,
syntactic, and discourse relations. A robust contextual representation is
learned for the entities, which serves as input to the relation classifier.
This simplifies handling of relations with arbitrary arity, and enables
multi-task learning with related relations. We evaluate this framework in two
important precision medicine settings, demonstrating its effectiveness with
both conventional supervised learning and distant supervision. Cross-sentence
extraction produced larger knowledge bases. and multi-task learning
significantly improved extraction accuracy. A thorough analysis of various LSTM
approaches yielded useful insight the impact of linguistic analysis on
extraction accuracy.
| 2,017 | Computation and Language |
Semi-supervised emotion lexicon expansion with label propagation and
specialized word embeddings | There exist two main approaches to automatically extract affective
orientation: lexicon-based and corpus-based. In this work, we argue that these
two methods are compatible and show that combining them can improve the
accuracy of emotion classifiers. In particular, we introduce a novel variant of
the Label Propagation algorithm that is tailored to distributed word
representations, we apply batch gradient descent to accelerate the optimization
of label propagation and to make the optimization feasible for large graphs,
and we propose a reproducible method for emotion lexicon expansion. We conclude
that label propagation can expand an emotion lexicon in a meaningful way and
that the expanded emotion lexicon can be leveraged to improve the accuracy of
an emotion classifier.
| 2,018 | Computation and Language |
Towards Speech Emotion Recognition "in the wild" using Aggregated
Corpora and Deep Multi-Task Learning | One of the challenges in Speech Emotion Recognition (SER) "in the wild" is
the large mismatch between training and test data (e.g. speakers and tasks). In
order to improve the generalisation capabilities of the emotion models, we
propose to use Multi-Task Learning (MTL) and use gender and naturalness as
auxiliary tasks in deep neural networks. This method was evaluated in
within-corpus and various cross-corpus classification experiments that simulate
conditions "in the wild". In comparison to Single-Task Learning (STL) based
state of the art methods, we found that our MTL method proposed improved
performance significantly. Particularly, models using both gender and
naturalness achieved more gains than those using either gender or naturalness
separately. This benefit was also found in the high-level representations of
the feature space, obtained from our method proposed, where discriminative
emotional clusters could be observed.
| 2,017 | Computation and Language |
Leveraging Sparse and Dense Feature Combinations for Sentiment
Classification | Neural networks are one of the most popular approaches for many natural
language processing tasks such as sentiment analysis. They often outperform
traditional machine learning models and achieve the state-of-art results on
most tasks. However, many existing deep learning models are complex, difficult
to train and provide a limited improvement over simpler methods. We propose a
simple, robust and powerful model for sentiment classification. This model
outperforms many deep learning models and achieves comparable results to other
deep learning models with complex architectures on sentiment analysis datasets.
We publish the code online.
| 2,017 | Computation and Language |
Data Sets: Word Embeddings Learned from Tweets and General Data | A word embedding is a low-dimensional, dense and real- valued vector
representation of a word. Word embeddings have been used in many NLP tasks.
They are usually gener- ated from a large text corpus. The embedding of a word
cap- tures both its syntactic and semantic aspects. Tweets are short, noisy and
have unique lexical and semantic features that are different from other types
of text. Therefore, it is necessary to have word embeddings learned
specifically from tweets. In this paper, we present ten word embedding data
sets. In addition to the data sets learned from just tweet data, we also built
embedding sets from the general data and the combination of tweets with the
general data. The general data consist of news articles, Wikipedia data and
other web data. These ten embedding models were learned from about 400 million
tweets and 7 billion words from the general text. In this paper, we also
present two experiments demonstrating how to use the data sets in some NLP
tasks, such as tweet sentiment analysis and tweet topic classification tasks.
| 2,017 | Computation and Language |
Sentiment Analysis by Joint Learning of Word Embeddings and Classifier | Word embeddings are representations of individual words of a text document in
a vector space and they are often use- ful for performing natural language pro-
cessing tasks. Current state of the art al- gorithms for learning word
embeddings learn vector representations from large corpora of text documents in
an unsu- pervised fashion. This paper introduces SWESA (Supervised Word
Embeddings for Sentiment Analysis), an algorithm for sentiment analysis via
word embeddings. SWESA leverages document label infor- mation to learn vector
representations of words from a modest corpus of text doc- uments by solving an
optimization prob- lem that minimizes a cost function with respect to both word
embeddings as well as classification accuracy. Analysis re- veals that SWESA
provides an efficient way of estimating the dimension of the word embeddings
that are to be learned. Experiments on several real world data sets show that
SWESA has superior per- formance when compared to previously suggested
approaches to word embeddings and sentiment analysis tasks.
| 2,018 | Computation and Language |
A Measure for Dialog Complexity and its Application in Streamlining
Service Operations | Dialog is a natural modality for interaction between customers and businesses
in the service industry. As customers call up the service provider, their
interactions may be routine or extraordinary. We believe that these
interactions, when seen as dialogs, can be analyzed to obtain a better
understanding of customer needs and how to efficiently address them. We
introduce the idea of a dialog complexity measure to characterize multi-party
interactions, propose a general data-driven method to calculate it, use it to
discover insights in public and enterprise dialog datasets, and demonstrate its
beneficial usage in facilitating better handling of customer requests and
evaluating service agents.
| 2,017 | Computation and Language |
Emotion Detection on TV Show Transcripts with Sequence-based
Convolutional Neural Networks | While there have been significant advances in detecting emotions from speech
and image recognition, emotion detection on text is still under-explored and
remained as an active research field. This paper introduces a corpus for
text-based emotion detection on multiparty dialogue as well as deep neural
models that outperform the existing approaches for document classification. We
first present a new corpus that provides annotation of seven emotions on
consecutive utterances in dialogues extracted from the show, Friends. We then
suggest four types of sequence-based convolutional neural network models with
attention that leverage the sequence information encapsulated in dialogue. Our
best model shows the accuracies of 37.9% and 54% for fine- and coarse-grained
emotions, respectively. Given the difficulty of this task, this is promising.
| 2,017 | Computation and Language |
Continuous Representation of Location for Geolocation and Lexical
Dialectology using Mixture Density Networks | We propose a method for embedding two-dimensional locations in a continuous
vector space using a neural network-based model incorporating mixtures of
Gaussian distributions, presenting two model variants for text-based
geolocation and lexical dialectology. Evaluated over Twitter data, the proposed
model outperforms conventional regression-based geolocation and provides a
better estimate of uncertainty. We also show the effectiveness of the
representation for predicting words from location in lexical dialectology, and
evaluate it using the DARE dataset.
| 2,017 | Computation and Language |
Fluency-Guided Cross-Lingual Image Captioning | Image captioning has so far been explored mostly in English, as most
available datasets are in this language. However, the application of image
captioning should not be restricted by language. Only few studies have been
conducted for image captioning in a cross-lingual setting. Different from these
works that manually build a dataset for a target language, we aim to learn a
cross-lingual captioning model fully from machine-translated sentences. To
conquer the lack of fluency in the translated sentences, we propose in this
paper a fluency-guided learning framework. The framework comprises a module to
automatically estimate the fluency of the sentences and another module to
utilize the estimated fluency scores to effectively train an image captioning
model for the target language. As experiments on two bilingual
(English-Chinese) datasets show, our approach improves both fluency and
relevance of the generated captions in Chinese, but without using any manually
written sentences from the target language.
| 2,017 | Computation and Language |
Extractive Summarization using Deep Learning | This paper proposes a text summarization approach for factual reports using a
deep learning model. This approach consists of three phases: feature
extraction, feature enhancement, and summary generation, which work together to
assimilate core information and generate a coherent, understandable summary. We
are exploring various features to improve the set of sentences selected for the
summary, and are using a Restricted Boltzmann Machine to enhance and abstract
those features to improve resultant accuracy without losing any important
information. The sentences are scored based on those enhanced features and an
extractive summary is constructed. Experimentation carried out on several
articles demonstrates the effectiveness of the proposed approach. Source code
available at: https://github.com/vagisha-nidhi/TextSummarizer
| 2,019 | Computation and Language |
Comparison of Decoding Strategies for CTC Acoustic Models | Connectionist Temporal Classification has recently attracted a lot of
interest as it offers an elegant approach to building acoustic models (AMs) for
speech recognition. The CTC loss function maps an input sequence of observable
feature vectors to an output sequence of symbols. Output symbols are
conditionally independent of each other under CTC loss, so a language model
(LM) can be incorporated conveniently during decoding, retaining the
traditional separation of acoustic and linguistic components in ASR. For fixed
vocabularies, Weighted Finite State Transducers provide a strong baseline for
efficient integration of CTC AMs with n-gram LMs. Character-based neural LMs
provide a straight forward solution for open vocabulary speech recognition and
all-neural models, and can be decoded with beam search. Finally,
sequence-to-sequence models can be used to translate a sequence of individual
sounds into a word string. We compare the performance of these three
approaches, and analyze their error patterns, which provides insightful
guidance for future research and development in this important area.
| 2,017 | Computation and Language |
Database of Parliamentary Speeches in Ireland, 1919-2013 | We present a database of parliamentary debates that contains the complete
record of parliamentary speeches from D\'ail \'Eireann, the lower house and
principal chamber of the Irish parliament, from 1919 to 2013. In addition, the
database contains background information on all TDs (Teachta D\'ala, members of
parliament), such as their party affiliations, constituencies and office
positions. The current version of the database includes close to 4.5 million
speeches from 1,178 TDs. The speeches were downloaded from the official
parliament website and further processed and parsed with a Python script.
Background information on TDs was collected from the member database of the
parliament website. Data on cabinet positions (ministers and junior ministers)
was collected from the official website of the government. A record linkage
algorithm and human coders were used to match TDs and ministers.
| 2,017 | Computation and Language |
Statistical Vs Rule Based Machine Translation; A Case Study on Indian
Language Perspective | In this paper we present our work on a case study between Statistical Machien
Transaltion (SMT) and Rule-Based Machine Translation (RBMT) systems on
English-Indian langugae and Indian to Indian langugae perspective. Main
objective of our study is to make a five way performance compariosn; such as,
a) SMT and RBMT b) SMT on English-Indian langugae c) RBMT on English-Indian
langugae d) SMT on Indian to Indian langugae perspective e) RBMT on Indian to
Indian langugae perspective. Through a detailed analysis we describe the Rule
Based and the Statistical Machine Translation system developments and its
evaluations. Through a detailed error analysis, we point out the relative
strengths and weaknesses of both systems. The observations based on our study
are: a) SMT systems outperforms RBMT b) In the case of SMT, English to Indian
language MT systmes performs better than Indian to English langugae MT systems
c) In the case of RBMT, English to Indian langugae MT systems perofrms better
than Indian to Englsih Language MT systems d) SMT systems performs better for
Indian to Indian language MT systems compared to RBMT. Effectively, we shall
see that even with a small amount of training corpus a statistical machine
translation system has many advantages for high quality domain specific machine
translation over that of a rule-based counterpart.
| 2,017 | Computation and Language |
Automatic Summarization of Online Debates | Debate summarization is one of the novel and challenging research areas in
automatic text summarization which has been largely unexplored. In this paper,
we develop a debate summarization pipeline to summarize key topics which are
discussed or argued in the two opposing sides of online debates. We view that
the generation of debate summaries can be achieved by clustering, cluster
labeling, and visualization. In our work, we investigate two different
clustering approaches for the generation of the summaries. In the first
approach, we generate the summaries by applying purely term-based clustering
and cluster labeling. The second approach makes use of X-means for clustering
and Mutual Information for labeling the clusters. Both approaches are driven by
ontologies. We visualize the results using bar charts. We think that our
results are a smooth entry for users aiming to receive the first impression
about what is discussed within a debate topic containing waste number of
argumentations.
| 2,017 | Computation and Language |
Gold Standard Online Debates Summaries and First Experiments Towards
Automatic Summarization of Online Debate Data | Usage of online textual media is steadily increasing. Daily, more and more
news stories, blog posts and scientific articles are added to the online
volumes. These are all freely accessible and have been employed extensively in
multiple research areas, e.g. automatic text summarization, information
retrieval, information extraction, etc. Meanwhile, online debate forums have
recently become popular, but have remained largely unexplored. For this reason,
there are no sufficient resources of annotated debate data available for
conducting research in this genre. In this paper, we collected and annotated
debate data for an automatic summarization task. Similar to extractive gold
standard summary generation our data contains sentences worthy to include into
a summary. Five human annotators performed this task. Inter-annotator
agreement, based on semantic similarity, is 36% for Cohen's kappa and 48% for
Krippendorff's alpha. Moreover, we also implement an extractive summarization
system for online debates and discuss prominent features for the task of
summarizing online debate data automatically.
| 2,017 | Computation and Language |
Identifying Harm Events in Clinical Care through Medical Narratives | Preventable medical errors are estimated to be among the leading causes of
injury and death in the United States. To prevent such errors, healthcare
systems have implemented patient safety and incident reporting systems. These
systems enable clinicians to report unsafe conditions and cases where patients
have been harmed due to errors in medical care. These reports are narratives in
natural language and while they provide detailed information about the
situation, it is non-trivial to perform large scale analysis for identifying
common causes of errors and harm to the patients. In this work, we present a
method based on attentive convolutional and recurrent networks for identifying
harm events in patient care and categorize the harm based on its severity
level. We demonstrate that our methods can significantly improve the
performance over existing methods in identifying harm in clinical care.
| 2,017 | Computation and Language |
Evaluating Word Embeddings for Sentence Boundary Detection in Speech
Transcripts | This paper is motivated by the automation of neuropsychological tests
involving discourse analysis in the retellings of narratives by patients with
potential cognitive impairment. In this scenario the task of sentence boundary
detection in speech transcripts is important as discourse analysis involves the
application of Natural Language Processing tools, such as taggers and parsers,
which depend on the sentence as a processing unit. Our aim in this paper is to
verify which embedding induction method works best for the sentence boundary
detection task, specifically whether it be those which were proposed to capture
semantic, syntactic or morphological similarities.
| 2,017 | Computation and Language |
Deconvolutional Paragraph Representation Learning | Learning latent representations from long text sequences is an important
first step in many natural language processing applications. Recurrent Neural
Networks (RNNs) have become a cornerstone for this challenging task. However,
the quality of sentences during RNN-based decoding (reconstruction) decreases
with the length of the text. We propose a sequence-to-sequence, purely
convolutional and deconvolutional autoencoding framework that is free of the
above issue, while also being computationally efficient. The proposed method is
simple, easy to implement and can be leveraged as a building block for many
applications. We show empirically that compared to RNNs, our framework is
better at reconstructing and correcting long paragraphs. Quantitative
evaluation on semi-supervised text classification and summarization tasks
demonstrate the potential for better utilization of long unlabeled text data.
| 2,017 | Computation and Language |
Learning Chinese Word Representations From Glyphs Of Characters | In this paper, we propose new methods to learn Chinese word representations.
Chinese characters are composed of graphical components, which carry rich
semantics. It is common for a Chinese learner to comprehend the meaning of a
word from these graphical components. As a result, we propose models that
enhance word representations by character glyphs. The character glyph features
are directly learned from the bitmaps of characters by convolutional
auto-encoder(convAE), and the glyph features improve Chinese word
representations which are already enhanced by character embeddings. Another
contribution in this paper is that we created several evaluation datasets in
traditional Chinese and made them public.
| 2,017 | Computation and Language |
Dialogue Act Segmentation for Vietnamese Human-Human Conversational
Texts | Dialog act identification plays an important role in understanding
conversations. It has been widely applied in many fields such as dialogue
systems, automatic machine translation, automatic speech recognition, and
especially useful in systems with human-computer natural language dialogue
interfaces such as virtual assistants and chatbots. The first step of
identifying dialog act is identifying the boundary of the dialog act in
utterances. In this paper, we focus on segmenting the utterance according to
the dialog act boundaries, i.e. functional segments identification, for
Vietnamese utterances. We investigate carefully functional segment
identification in two approaches: (1) machine learning approach using maximum
entropy (ME) and conditional random fields (CRFs); (2) deep learning approach
using bidirectional Long Short-Term Memory (LSTM) with a CRF layer
(Bi-LSTM-CRF) on two different conversational datasets: (1) Facebook messages
(Message data); (2) transcription from phone conversations (Phone data). To the
best of our knowledge, this is the first work that applies deep learning based
approach to dialog act segmentation. As the results show, deep learning
approach performs appreciably better as to compare with traditional machine
learning approaches. Moreover, it is also the first study that tackles dialog
act and functional segment identification for Vietnamese.
| 2,017 | Computation and Language |
mAnI: Movie Amalgamation using Neural Imitation | Cross-modal data retrieval has been the basis of various creative tasks
performed by Artificial Intelligence (AI). One such highly challenging task for
AI is to convert a book into its corresponding movie, which most of the
creative film makers do as of today. In this research, we take the first step
towards it by visualizing the content of a book using its corresponding movie
visuals. Given a set of sentences from a book or even a fan-fiction written in
the same universe, we employ deep learning models to visualize the input by
stitching together relevant frames from the movie. We studied and compared
three different types of setting to match the book with the movie content: (i)
Dialog model: using only the dialog from the movie, (ii) Visual model: using
only the visual content from the movie, and (iii) Hybrid model: using the
dialog and the visual content from the movie. Experiments on the publicly
available MovieBook dataset shows the effectiveness of the proposed models.
| 2,017 | Computation and Language |
Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding | Entity alignment is the task of finding entities in two knowledge bases (KBs)
that represent the same real-world object. When facing KBs in different natural
languages, conventional cross-lingual entity alignment methods rely on machine
translation to eliminate the language barriers. These approaches often suffer
from the uneven quality of translations between languages. While recent
embedding-based techniques encode entities and relationships in KBs and do not
need machine translation for cross-lingual entity alignment, a significant
number of attributes remain largely unexplored. In this paper, we propose a
joint attribute-preserving embedding model for cross-lingual entity alignment.
It jointly embeds the structures of two KBs into a unified vector space and
further refines it by leveraging attribute correlations in the KBs. Our
experimental results on real-world datasets show that this approach
significantly outperforms the state-of-the-art embedding approaches for
cross-lingual entity alignment and could be complemented with methods based on
machine translation.
| 2,017 | Computation and Language |
Learning spectro-temporal features with 3D CNNs for speech emotion
recognition | In this paper, we propose to use deep 3-dimensional convolutional networks
(3D CNNs) in order to address the challenge of modelling spectro-temporal
dynamics for speech emotion recognition (SER). Compared to a hybrid of
Convolutional Neural Network and Long-Short-Term-Memory (CNN-LSTM), our
proposed 3D CNNs simultaneously extract short-term and long-term spectral
features with a moderate number of parameters. We evaluated our proposed and
other state-of-the-art methods in a speaker-independent manner using aggregated
corpora that give a large and diverse set of speakers. We found that 1) shallow
temporal and moderately deep spectral kernels of a homogeneous architecture are
optimal for the task; and 2) our 3D CNNs are more effective for
spectro-temporal feature learning compared to other methods. Finally, we
visualised the feature space obtained with our proposed method using
t-distributed stochastic neighbour embedding (T-SNE) and could observe distinct
clusters of emotions.
| 2,017 | Computation and Language |
Natural Language Processing: State of The Art, Current Trends and
Challenges | Natural language processing (NLP) has recently gained much attention for
representing and analysing human language computationally. It has spread its
applications in various fields such as machine translation, email spam
detection, information extraction, summarization, medical, and question
answering etc. The paper distinguishes four phases by discussing different
levels of NLP and components of Natural Language Generation (NLG) followed by
presenting the history and evolution of NLP, state of the art presenting the
various applications of NLP and current trends and challenges.
| 2,022 | Computation and Language |
Towards Syntactic Iberian Polarity Classification | Lexicon-based methods using syntactic rules for polarity classification rely
on parsers that are dependent on the language and on treebank guidelines. Thus,
rules are also dependent and require adaptation, especially in multilingual
scenarios. We tackle this challenge in the context of the Iberian Peninsula,
releasing the first symbolic syntax-based Iberian system with rules shared
across five official languages: Basque, Catalan, Galician, Portuguese and
Spanish. The model is made available.
| 2,017 | Computation and Language |
Simple Open Stance Classification for Rumour Analysis | Stance classification determines the attitude, or stance, in a (typically
short) text. The task has powerful applications, such as the detection of fake
news or the automatic extraction of attitudes toward entities or events in the
media. This paper describes a surprisingly simple and efficient classification
approach to open stance classification in Twitter, for rumour and veracity
classification. The approach profits from a novel set of automatically
identifiable problem-specific features, which significantly boost classifier
accuracy and achieve above state-of-the-art results on recent benchmark
datasets. This calls into question the value of using complex sophisticated
models for stance classification without first doing informed feature
extraction.
| 2,017 | Computation and Language |
An Annotated Corpus of Relational Strategies in Customer Service | We create and release the first publicly available commercial customer
service corpus with annotated relational segments. Human-computer data from
three live customer service Intelligent Virtual Agents (IVAs) in the domains of
travel and telecommunications were collected, and reviewers marked all text
that was deemed unnecessary to the determination of user intention. After
merging the selections of multiple reviewers to create highlighted texts, a
second round of annotation was done to determine the classes of language
present in the highlighted sections such as the presence of Greetings,
Backstory, Justification, Gratitude, Rants, or Emotions. This resulting corpus
is a valuable resource for improving the quality and relational abilities of
IVAs. As well as discussing the corpus itself, we compare the usage of such
language in human-human interactions on TripAdvisor forums. We show that
removal of this language from task-based inputs has a positive effect on IVA
understanding by both an increase in confidence and improvement in responses,
demonstrating the need for automated methods of its discovery.
| 2,017 | Computation and Language |
Large-Scale Domain Adaptation via Teacher-Student Learning | High accuracy speech recognition requires a large amount of transcribed data
for supervised training. In the absence of such data, domain adaptation of a
well-trained acoustic model can be performed, but even here, high accuracy
usually requires significant labeled data from the target domain. In this work,
we propose an approach to domain adaptation that does not require
transcriptions but instead uses a corpus of unlabeled parallel data, consisting
of pairs of samples from the source domain of the well-trained model and the
desired target domain. To perform adaptation, we employ teacher/student (T/S)
learning, in which the posterior probabilities generated by the source-domain
model can be used in lieu of labels to train the target-domain model. We
evaluate the proposed approach in two scenarios, adapting a clean acoustic
model to noisy speech and adapting an adults speech acoustic model to children
speech. Significant improvements in accuracy are obtained, with reductions in
word error rate of up to 44% over the original source model without the need
for transcribed data in the target domain. Moreover, we show that increasing
the amount of unlabeled data results in additional model robustness, which is
particularly beneficial when using simulated training data in the
target-domain.
| 2,017 | Computation and Language |
A Question Answering Approach to Emotion Cause Extraction | Emotion cause extraction aims to identify the reasons behind a certain
emotion expressed in text. It is a much more difficult task compared to emotion
classification. Inspired by recent advances in using deep memory networks for
question answering (QA), we propose a new approach which considers emotion
cause identification as a reading comprehension task in QA. Inspired by
convolutional neural networks, we propose a new mechanism to store relevant
context in different memory slots to model context information. Our proposed
approach can extract both word level sequence features and lexical features.
Performance evaluation shows that our method achieves the state-of-the-art
performance on a recently released emotion cause dataset, outperforming a
number of competitive baselines by at least 3.01% in F-measure.
| 2,017 | Computation and Language |
Syllable-level Neural Language Model for Agglutinative Language | Language models for agglutinative languages have always been hindered in past
due to myriad of agglutinations possible to any given word through various
affixes. We propose a method to diminish the problem of out-of-vocabulary words
by introducing an embedding derived from syllables and morphemes which
leverages the agglutinative property. Our model outperforms character-level
embedding in perplexity by 16.87 with 9.50M parameters. Proposed method
achieves state of the art performance over existing input prediction methods in
terms of Key Stroke Saving and has been commercialized.
| 2,017 | Computation and Language |
EmoAtt at EmoInt-2017: Inner attention sentence embedding for Emotion
Intensity | In this paper we describe a deep learning system that has been designed and
built for the WASSA 2017 Emotion Intensity Shared Task. We introduce a
representation learning approach based on inner attention on top of an RNN.
Results show that our model offers good capabilities and is able to
successfully identify emotion-bearing words to predict intensity without
leveraging on lexicons, obtaining the 13th place among 22 shared task
competitors.
| 2,017 | Computation and Language |
Assessing the Stylistic Properties of Neurally Generated Text in
Authorship Attribution | Recent applications of neural language models have led to an increased
interest in the automatic generation of natural language. However impressive,
the evaluation of neurally generated text has so far remained rather informal
and anecdotal. Here, we present an attempt at the systematic assessment of one
aspect of the quality of neurally generated text. We focus on a specific aspect
of neural language generation: its ability to reproduce authorial writing
styles. Using established models for authorship attribution, we empirically
assess the stylistic qualities of neurally generated text. In comparison to
conventional language models, neural models generate fuzzier text that is
relatively harder to attribute correctly. Nevertheless, our results also
suggest that neurally generated text offers more valuable perspectives for the
augmentation of training data.
| 2,017 | Computation and Language |
Agree to Disagree: Improving Disagreement Detection with Dual GRUs | This paper presents models for detecting agreement/disagreement in online
discussions. In this work we show that by using a Siamese inspired architecture
to encode the discussions, we no longer need to rely on hand-crafted features
to exploit the meta thread structure. We evaluate our model on existing online
discussion corpora - ABCD, IAC and AWTP. Experimental results on ABCD dataset
show that by fusing lexical and word embedding features, our model achieves the
state of the art performance of 0.804 average F1 score. We also show that the
model trained on ABCD dataset performs competitively on relatively smaller
annotated datasets (IAC and AWTP).
| 2,017 | Computation and Language |
Future Word Contexts in Neural Network Language Models | Recently, bidirectional recurrent network language models (bi-RNNLMs) have
been shown to outperform standard, unidirectional, recurrent neural network
language models (uni-RNNLMs) on a range of speech recognition tasks. This
indicates that future word context information beyond the word history can be
useful. However, bi-RNNLMs pose a number of challenges as they make use of the
complete previous and future word context information. This impacts both
training efficiency and their use within a lattice rescoring framework. In this
paper these issues are addressed by proposing a novel neural network structure,
succeeding word RNNLMs (su-RNNLMs). Instead of using a recurrent unit to
capture the complete future word contexts, a feedforward unit is used to model
a finite number of succeeding, future, words. This model can be trained much
more efficiently than bi-RNNLMs and can also be used for lattice rescoring.
Experimental results on a meeting transcription task (AMI) show the proposed
model consistently outperformed uni-RNNLMs and yield only a slight degradation
compared to bi-RNNLMs in N-best rescoring. Additionally, performance
improvements can be obtained using lattice rescoring and subsequent confusion
network decoding.
| 2,017 | Computation and Language |
An Improved Residual LSTM Architecture for Acoustic Modeling | Long Short-Term Memory (LSTM) is the primary recurrent neural networks
architecture for acoustic modeling in automatic speech recognition systems.
Residual learning is an efficient method to help neural networks converge
easier and faster. In this paper, we propose several types of residual LSTM
methods for our acoustic modeling. Our experiments indicate that, compared with
classic LSTM, our architecture shows more than 8% relative reduction in Phone
Error Rate (PER) on TIMIT tasks. At the same time, our residual fast LSTM
approach shows 4% relative reduction in PER on the same task. Besides, we find
that all this architecture could have good results on THCHS-30, Librispeech and
Switchboard corpora.
| 2,017 | Computation and Language |
Cross-Lingual Dependency Parsing for Closely Related Languages -
Helsinki's Submission to VarDial 2017 | This paper describes the submission from the University of Helsinki to the
shared task on cross-lingual dependency parsing at VarDial 2017. We present
work on annotation projection and treebank translation that gave good results
for all three target languages in the test set. In particular, Slovak seems to
work well with information coming from the Czech treebank, which is in line
with related work. The attachment scores for cross-lingual models even surpass
the fully supervised models trained on the target language treebank. Croatian
is the most difficult language in the test set and the improvements over the
baseline are rather modest. Norwegian works best with information coming from
Swedish whereas Danish contributes surprisingly little.
| 2,017 | Computation and Language |
Neural machine translation for low-resource languages | Neural machine translation (NMT) approaches have improved the state of the
art in many machine translation settings over the last couple of years, but
they require large amounts of training data to produce sensible output. We
demonstrate that NMT can be used for low-resource languages as well, by
introducing more local dependencies and using word alignments to learn sentence
reordering during translation. In addition to our novel model, we also present
an empirical evaluation of low-resource phrase-based statistical machine
translation (SMT) and NMT to investigate the lower limits of the respective
technologies. We find that while SMT remains the best option for low-resource
settings, our method can produce acceptable translations with only 70000 tokens
of training data, a level where the baseline NMT system fails completely.
| 2,017 | Computation and Language |
The Natural Stories Corpus | It is now a common practice to compare models of human language processing by
predicting participant reactions (such as reading times) to corpora consisting
of rich naturalistic linguistic materials. However, many of the corpora used in
these studies are based on naturalistic text and thus do not contain many of
the low-frequency syntactic constructions that are often required to
distinguish processing theories. Here we describe a new corpus consisting of
English texts edited to contain many low-frequency syntactic constructions
while still sounding fluent to native speakers. The corpus is annotated with
hand-corrected parse trees and includes self-paced reading time data. Here we
give an overview of the content of the corpus and release the data.
| 2,017 | Computation and Language |
CLaC @ QATS: Quality Assessment for Text Simplification | This paper describes our approach to the 2016 QATS quality assessment shared
task. We trained three independent Random Forest classifiers in order to assess
the quality of the simplified texts in terms of grammaticality, meaning
preservation and simplicity. We used the language model of Google-Ngram as
feature to predict the grammaticality. Meaning preservation is predicted using
two complementary approaches based on word embedding and WordNet synonyms. A
wider range of features including TF-IDF, sentence length and frequency of cue
phrases are used to evaluate the simplicity aspect. Overall, the accuracy of
the system ranges from 33.33% for the overall aspect to 58.73% for
grammaticality.
| 2,017 | Computation and Language |
The CLaC Discourse Parser at CoNLL-2016 | This paper describes our submission "CLaC" to the CoNLL-2016 shared task on
shallow discourse parsing. We used two complementary approaches for the task. A
standard machine learning approach for the parsing of explicit relations, and a
deep learning approach for non-explicit relations. Overall, our parser achieves
an F1-score of 0.2106 on the identification of discourse relations (0.3110 for
explicit relations and 0.1219 for non-explicit relations) on the blind
CoNLL-2016 test set.
| 2,017 | Computation and Language |
On the Contribution of Discourse Structure on Text Complexity Assessment | This paper investigates the influence of discourse features on text
complexity assessment. To do so, we created two data sets based on the Penn
Discourse Treebank and the Simple English Wikipedia corpora and compared the
influence of coherence, cohesion, surface, lexical and syntactic features to
assess text complexity.
Results show that with both data sets coherence features are more correlated
to text complexity than the other types of features. In addition, feature
selection revealed that with both data sets the top most discriminating feature
is a coherence feature.
| 2,017 | Computation and Language |
ClaC: Semantic Relatedness of Words and Phrases | The measurement of phrasal semantic relatedness is an important metric for
many natural language processing applications. In this paper, we present three
approaches for measuring phrasal semantics, one based on a semantic network
model, another on a distributional similarity model, and a hybrid between the
two. Our hybrid approach achieved an F-measure of 77.4% on the task of
evaluating the semantic similarity of words and compositional phrases.
| 2,017 | Computation and Language |
Measuring the Effect of Discourse Relations on Blog Summarization | The work presented in this paper attempts to evaluate and quantify the use of
discourse relations in the context of blog summarization and compare their use
to more traditional and factual texts. Specifically, we measured the usefulness
of 6 discourse relations - namely comparison, contingency, illustration,
attribution, topic-opinion, and attributive for the task of text summarization
from blogs. We have evaluated the effect of each relation using the TAC 2008
opinion summarization dataset and compared them with the results with the DUC
2007 dataset. The results show that in both textual genres, contingency,
comparison, and illustration relations provide a significant improvement on
summarization content; while attribution, topic-opinion, and attributive
relations do not provide a consistent and significant improvement. These
results indicate that, at least for summarization, discourse relations are just
as useful for informal and affective texts as for more traditional news
articles.
| 2,017 | Computation and Language |
The CLaC Discourse Parser at CoNLL-2015 | This paper describes our submission (kosseim15) to the CoNLL-2015 shared task
on shallow discourse parsing. We used the UIMA framework to develop our parser
and used ClearTK to add machine learning functionality to the UIMA framework.
Overall, our parser achieves a result of 17.3 F1 on the identification of
discourse relations on the blind CoNLL-2015 test set, ranking in sixth place.
| 2,017 | Computation and Language |
What Drives the International Development Agenda? An NLP Analysis of the
United Nations General Debate 1970-2016 | There is surprisingly little known about agenda setting for international
development in the United Nations (UN) despite it having a significant
influence on the process and outcomes of development efforts. This paper
addresses this shortcoming using a novel approach that applies natural language
processing techniques to countries' annual statements in the UN General Debate.
Every year UN member states deliver statements during the General Debate on
their governments' perspective on major issues in world politics. These
speeches provide invaluable information on state preferences on a wide range of
issues, including international development, but have largely been overlooked
in the study of global politics. This paper identifies the main international
development topics that states raise in these speeches between 1970 and 2016,
and examine the country-specific drivers of international development rhetoric.
| 2,017 | Computation and Language |
Arabic Multi-Dialect Segmentation: bi-LSTM-CRF vs. SVM | Arabic word segmentation is essential for a variety of NLP applications such
as machine translation and information retrieval. Segmentation entails breaking
words into their constituent stems, affixes and clitics. In this paper, we
compare two approaches for segmenting four major Arabic dialects using only
several thousand training examples for each dialect. The two approaches involve
posing the problem as a ranking problem, where an SVM ranker picks the best
segmentation, and as a sequence labeling problem, where a bi-LSTM RNN coupled
with CRF determines where best to segment words. We are able to achieve solid
segmentation results for all dialects using rather limited training data. We
also show that employing Modern Standard Arabic data for domain adaptation and
assuming context independence improve overall results.
| 2,017 | Computation and Language |
The Helsinki Neural Machine Translation System | We introduce the Helsinki Neural Machine Translation system (HNMT) and how it
is applied in the news translation task at WMT 2017, where it ranked first in
both the human and automatic evaluations for English--Finnish. We discuss the
success of English--Finnish translations and the overall advantage of NMT over
a strong SMT baseline. We also discuss our submissions for English--Latvian,
English--Chinese and Chinese--English.
| 2,017 | Computation and Language |
Neural Machine Translation with Extended Context | We investigate the use of extended context in attention-based neural machine
translation. We base our experiments on translated movie subtitles and discuss
the effect of increasing the segments beyond single translation units. We study
the use of extended source language context as well as bilingual context
extensions. The models learn to distinguish between information from different
segments and are surprisingly robust with respect to translation quality. In
this pilot study, we observe interesting cross-sentential attention patterns
that improve textual coherence in translation at least in some selected cases.
| 2,017 | Computation and Language |
An End-to-End Trainable Neural Network Model with Belief Tracking for
Task-Oriented Dialog | We present a novel end-to-end trainable neural network model for
task-oriented dialog systems. The model is able to track dialog state, issue
API calls to knowledge base (KB), and incorporate structured KB query results
into system responses to successfully complete task-oriented dialogs. The
proposed model produces well-structured system responses by jointly learning
belief tracking and KB result processing conditioning on the dialog history. We
evaluate the model in a restaurant search domain using a dataset that is
converted from the second Dialog State Tracking Challenge (DSTC2) corpus.
Experiment results show that the proposed model can robustly track dialog state
given the dialog history. Moreover, our model demonstrates promising results in
producing appropriate system responses, outperforming prior end-to-end
trainable neural network models using per-response accuracy evaluation metrics.
| 2,017 | Computation and Language |
Expanding Abbreviations in a Strongly Inflected Language: Are
Morphosyntactic Tags Sufficient? | In this paper, the problem of recovery of morphological information lost in
abbreviated forms is addressed with a focus on highly inflected languages.
Evidence is presented that the correct inflected form of an expanded
abbreviation can in many cases be deduced solely from the morphosyntactic tags
of the context. The prediction model is a deep bidirectional LSTM network with
tag embedding. The training and evaluation data are gathered by finding the
words which could have been abbreviated and using their corresponding
morphosyntactic tags as the labels, while the tags of the context words are
used as the input features for classification. The network is trained on over
10 million words from the Polish Sejm Corpus and achieves 74.2% prediction
accuracy on a smaller, but more general National Corpus of Polish. The analysis
of errors suggests that performance in this task may improve if some prior
knowledge about the abbreviated word is incorporated into the model.
| 2,018 | Computation and Language |
A Batch Noise Contrastive Estimation Approach for Training Large
Vocabulary Language Models | Training large vocabulary Neural Network Language Models (NNLMs) is a
difficult task due to the explicit requirement of the output layer
normalization, which typically involves the evaluation of the full softmax
function over the complete vocabulary. This paper proposes a Batch Noise
Contrastive Estimation (B-NCE) approach to alleviate this problem. This is
achieved by reducing the vocabulary, at each time step, to the target words in
the batch and then replacing the softmax by the noise contrastive estimation
approach, where these words play the role of targets and noise samples at the
same time. In doing so, the proposed approach can be fully formulated and
implemented using optimal dense matrix operations. Applying B-NCE to train
different NNLMs on the Large Text Compression Benchmark (LTCB) and the One
Billion Word Benchmark (OBWB) shows a significant reduction of the training
time with no noticeable degradation of the models performance. This paper also
presents a new baseline comparative study of different standard NNLMs on the
large OBWB on a single Titan-X GPU.
| 2,017 | Computation and Language |
Learning to Paraphrase for Question Answering | Question answering (QA) systems are sensitive to the many different ways
natural language expresses the same information need. In this paper we turn to
paraphrases as a means of capturing this knowledge and present a general
framework which learns felicitous paraphrases for various QA tasks. Our method
is trained end-to-end using question-answer pairs as a supervision signal. A
question and its paraphrases serve as input to a neural scoring model which
assigns higher weights to linguistic expressions most likely to yield correct
answers. We evaluate our approach on QA over Freebase and answer sentence
selection. Experimental results on three datasets show that our framework
consistently improves performance, achieving competitive results despite the
use of simple QA models.
| 2,017 | Computation and Language |
Portuguese Word Embeddings: Evaluating on Word Analogies and Natural
Language Tasks | Word embeddings have been found to provide meaningful representations for
words in an efficient way; therefore, they have become common in Natural
Language Processing sys- tems. In this paper, we evaluated different word
embedding models trained on a large Portuguese corpus, including both Brazilian
and European variants. We trained 31 word embedding models using FastText,
GloVe, Wang2Vec and Word2Vec. We evaluated them intrinsically on syntactic and
semantic analogies and extrinsically on POS tagging and sentence semantic
similarity tasks. The obtained results suggest that word analogies are not
appropriate for word embedding evaluation; task-specific evaluations appear to
be a better option.
| 2,017 | Computation and Language |
Vector Space Model as Cognitive Space for Text Classification | In this era of digitization, knowing the user's sociolect aspects have become
essential features to build the user specific recommendation systems. These
sociolect aspects could be found by mining the user's language sharing in the
form of text in social media and reviews. This paper describes about the
experiment that was performed in PAN Author Profiling 2017 shared task. The
objective of the task is to find the sociolect aspects of the users from their
tweets. The sociolect aspects considered in this experiment are user's gender
and native language information. Here user's tweets written in a different
language from their native language are represented as Document - Term Matrix
with document frequency as the constraint. Further classification is done using
the Support Vector Machine by taking gender and native language as target
classes. This experiment attains the average accuracy of 73.42% in gender
prediction and 76.26% in the native language identification task.
| 2,018 | Computation and Language |
The Microsoft 2017 Conversational Speech Recognition System | We describe the 2017 version of Microsoft's conversational speech recognition
system, in which we update our 2016 system with recent developments in
neural-network-based acoustic and language modeling to further advance the
state of the art on the Switchboard speech recognition task. The system adds a
CNN-BLSTM acoustic model to the set of model architectures we combined
previously, and includes character-based and dialog session aware LSTM language
models in rescoring. For system combination we adopt a two-stage approach,
whereby subsets of acoustic models are first combined at the senone/frame
level, followed by a word-level voting via confusion networks. We also added a
confusion network rescoring step after system combination. The resulting system
yields a 5.1\% word error rate on the 2000 Switchboard evaluation set.
| 2,018 | Computation and Language |
Scientific Information Extraction with Semi-supervised Neural Tagging | This paper addresses the problem of extracting keyphrases from scientific
articles and categorizing them as corresponding to a task, process, or
material. We cast the problem as sequence tagging and introduce semi-supervised
methods to a neural tagging model, which builds on recent advances in named
entity recognition. Since annotated training data is scarce in this domain, we
introduce a graph-based semi-supervised algorithm together with a data
selection scheme to leverage unannotated articles. Both inductive and
transductive semi-supervised learning strategies outperform state-of-the-art
information extraction performance on the 2017 SemEval Task 10 ScienceIE task.
| 2,017 | Computation and Language |
Seernet at EmoInt-2017: Tweet Emotion Intensity Estimator | The paper describes experiments on estimating emotion intensity in tweets
using a generalized regressor system. The system combines lexical, syntactic
and pre-trained word embedding features, trains them on general regressors and
finally combines the best performing models to create an ensemble. The proposed
system stood 3rd out of 22 systems in the leaderboard of WASSA-2017 Shared Task
on Emotion Intensity.
| 2,017 | Computation and Language |
Cold Fusion: Training Seq2Seq Models Together with Language Models | Sequence-to-sequence (Seq2Seq) models with attention have excelled at tasks
which involve generating natural language sentences such as machine
translation, image captioning and speech recognition. Performance has further
been improved by leveraging unlabeled data, often in the form of a language
model. In this work, we present the Cold Fusion method, which leverages a
pre-trained language model during training, and show its effectiveness on the
speech recognition task. We show that Seq2Seq models with Cold Fusion are able
to better utilize language information enjoying i) faster convergence and
better generalization, and ii) almost complete transfer to a new domain while
using less than 10% of the labeled training data.
| 2,017 | Computation and Language |
Handling Homographs in Neural Machine Translation | Homographs, words with different meanings but the same surface form, have
long caused difficulty for machine translation systems, as it is difficult to
select the correct translation based on the context. However, with the advent
of neural machine translation (NMT) systems, which can theoretically take into
account global sentential context, one may hypothesize that this problem has
been alleviated. In this paper, we first provide empirical evidence that
existing NMT systems in fact still have significant problems in properly
translating ambiguous words. We then proceed to describe methods, inspired by
the word sense disambiguation literature, that model the context of the input
word with context-aware word embeddings that help to differentiate the word
sense be- fore feeding it into the encoder. Experiments on three language pairs
demonstrate that such models improve the performance of NMT systems both in
terms of BLEU score and in the accuracy of translating homographs.
| 2,018 | Computation and Language |
Golden Years, Golden Shores: A Study of Elders in Online Travel
Communities | In this paper we present our exploratory findings related to extracting
knowledge and experiences from a community of senior tourists. By using tools
of qualitative analysis as well as review of literature, we managed to verify a
set of hypotheses related to the content created by senior tourists when
participating in on-line communities. We also produced a codebook, representing
various themes one may encounter in such communities. This codebook, derived
from our own qualitative research, as well a literature review will serve as a
basis for further development of automated tools of knowledge extraction. We
also managed to find that older adults more often than other poster in tourists
forums, mention their age in discussion, more often share their experiences and
motivation to travel, however they do not differ in relation to describing
barriers encountered while traveling.
| 2,017 | Computation and Language |
Long-Short Range Context Neural Networks for Language Modeling | The goal of language modeling techniques is to capture the statistical and
structural properties of natural languages from training corpora. This task
typically involves the learning of short range dependencies, which generally
model the syntactic properties of a language and/or long range dependencies,
which are semantic in nature. We propose in this paper a new multi-span
architecture, which separately models the short and long context information
while it dynamically merges them to perform the language modeling task. This is
done through a novel recurrent Long-Short Range Context (LSRC) network, which
explicitly models the local (short) and global (long) context using two
separate hidden states that evolve in time. This new architecture is an
adaptation of the Long-Short Term Memory network (LSTM) to take into account
the linguistic properties. Extensive experiments conducted on the Penn Treebank
(PTB) and the Large Text Compression Benchmark (LTCB) corpus showed a
significant reduction of the perplexity when compared to state-of-the-art
language modeling techniques.
| 2,017 | Computation and Language |
A rule based algorithm for detecting negative words in Persian | In this paper, we present a novel method for detecting negative words in
Persian. We first used an algorithm to an exceptions list which was later
modified by hand. We then used the mentioned lists and a Persian polarity
corpus in our rule based algorithm to detect negative words.
| 2,017 | Computation and Language |
Classification of Radiology Reports Using Neural Attention Models | The electronic health record (EHR) contains a large amount of
multi-dimensional and unstructured clinical data of significant operational and
research value. Distinguished from previous studies, our approach embraces a
double-annotated dataset and strays away from obscure "black-box" models to
comprehensive deep learning models. In this paper, we present a novel neural
attention mechanism that not only classifies clinically important findings.
Specifically, convolutional neural networks (CNN) with attention analysis are
used to classify radiology head computed tomography reports based on five
categories that radiologists would account for in assessing acute and
communicable findings in daily practice. The experiments show that our CNN
attention models outperform non-neural models, especially when trained on a
larger dataset. Our attention analysis demonstrates the intuition behind the
classifier's decision by generating a heatmap that highlights attended terms
used by the CNN model; this is valuable when potential downstream medical
decisions are to be performed by human experts or the classifier information is
to be used in cohort construction such as for epidemiological studies.
| 2,017 | Computation and Language |
A Neural Network Approach for Mixing Language Models | The performance of Neural Network (NN)-based language models is steadily
improving due to the emergence of new architectures, which are able to learn
different natural language characteristics. This paper presents a novel
framework, which shows that a significant improvement can be achieved by
combining different existing heterogeneous models in a single architecture.
This is done through 1) a feature layer, which separately learns different
NN-based models and 2) a mixture layer, which merges the resulting model
features. In doing so, this architecture benefits from the learning
capabilities of each model with no noticeable increase in the number of model
parameters or the training time. Extensive experiments conducted on the Penn
Treebank (PTB) and the Large Text Compression Benchmark (LTCB) corpus showed a
significant reduction of the perplexity when compared to state-of-the-art
feedforward as well as recurrent neural network architectures.
| 2,017 | Computation and Language |
Automatic Detection of Fake News | The proliferation of misleading information in everyday access media outlets
such as social media feeds, news blogs, and online newspapers have made it
challenging to identify trustworthy news sources, thus increasing the need for
computational tools able to provide insights into the reliability of online
content. In this paper, we focus on the automatic identification of fake
content in online news. Our contribution is twofold. First, we introduce two
novel datasets for the task of fake news detection, covering seven different
news domains. We describe the collection, annotation, and validation process in
detail and present several exploratory analysis on the identification of
linguistic differences in fake and legitimate news content. Second, we conduct
a set of learning experiments to build accurate fake news detectors. In
addition, we provide comparative analyses of the automatic and manual
identification of fake news.
| 2,017 | Computation and Language |
Towards an Automatic Turing Test: Learning to Evaluate Dialogue
Responses | Automatically evaluating the quality of dialogue responses for unstructured
domains is a challenging problem. Unfortunately, existing automatic evaluation
metrics are biased and correlate very poorly with human judgements of response
quality. Yet having an accurate automatic evaluation procedure is crucial for
dialogue research, as it allows rapid prototyping and testing of new models
with fewer expensive human evaluations. In response to this challenge, we
formulate automatic dialogue evaluation as a learning problem. We present an
evaluation model (ADEM) that learns to predict human-like scores to input
responses, using a new dataset of human response scores. We show that the ADEM
model's predictions correlate significantly, and at a level much higher than
word-overlap metrics such as BLEU, with human judgements at both the utterance
and system-level. We also show that ADEM can generalize to evaluating dialogue
models unseen during training, an important step for automatic dialogue
evaluation.
| 2,017 | Computation and Language |
NNVLP: A Neural Network-Based Vietnamese Language Processing Toolkit | This paper demonstrates neural network-based toolkit namely NNVLP for
essential Vietnamese language processing tasks including part-of-speech (POS)
tagging, chunking, named entity recognition (NER). Our toolkit is a combination
of bidirectional Long Short-Term Memory (Bi-LSTM), Convolutional Neural Network
(CNN), Conditional Random Field (CRF), using pre-trained word embeddings as
input, which achieves state-of-the-art results on these three tasks. We provide
both API and web demo for this toolkit.
| 2,017 | Computation and Language |
A Study on Neural Network Language Modeling | An exhaustive study on neural network language modeling (NNLM) is performed
in this paper. Different architectures of basic neural network language models
are described and examined. A number of different improvements over basic
neural network language models, including importance sampling, word classes,
caching and bidirectional recurrent neural network (BiRNN), are studied
separately, and the advantages and disadvantages of every technique are
evaluated. Then, the limits of neural network language modeling are explored
from the aspects of model architecture and knowledge representation. Part of
the statistical information from a word sequence will loss when it is processed
word by word in a certain order, and the mechanism of training neural network
by updating weight matrixes and vectors imposes severe restrictions on any
significant enhancement of NNLM. For knowledge representation, the knowledge
represented by neural network language models is the approximate probabilistic
distribution of word sequences from a certain training data set rather than the
knowledge of a language itself or the information conveyed by word sequences in
a natural language. Finally, some directions for improving neural network
language modeling further is discussed.
| 2,017 | Computation and Language |
An Image Analysis Approach to the Calligraphy of Books | Text network analysis has received increasing attention as a consequence of
its wide range of applications. In this work, we extend a previous work founded
on the study of topological features of mesoscopic networks. Here, the
geometrical properties of visualized networks are quantified in terms of
several image analysis techniques and used as subsidies for authorship
attribution. It was found that the visual features account for performance
similar to that achieved by using topological measurements. In addition, the
combination of these two types of features improved the performance.
| 2,018 | Computation and Language |
Combining Discrete and Neural Features for Sequence Labeling | Neural network models have recently received heated research attention in the
natural language processing community. Compared with traditional models with
discrete features, neural models have two main advantages. First, they take
low-dimensional, real-valued embedding vectors as inputs, which can be trained
over large raw data, thereby addressing the issue of feature sparsity in
discrete models. Second, deep neural networks can be used to automatically
combine input features, and including non-local features that capture semantic
patterns that cannot be expressed using discrete indicator features. As a
result, neural network models have achieved competitive accuracies compared
with the best discrete models for a range of NLP tasks.
On the other hand, manual feature templates have been carefully investigated
for most NLP tasks over decades and typically cover the most useful indicator
pattern for solving the problems. Such information can be complementary the
features automatically induced from neural networks, and therefore combining
discrete and neural features can potentially lead to better accuracy compared
with models that leverage discrete or neural features only.
In this paper, we systematically investigate the effect of discrete and
neural feature combination for a range of fundamental NLP tasks based on
sequence labeling, including word segmentation, POS tagging and named entity
recognition for Chinese and English, respectively. Our results on standard
benchmarks show that state-of-the-art neural models can give accuracies
comparable to the best discrete models in the literature for most tasks and
combing discrete and neural features unanimously yield better results.
| 2,017 | Computation and Language |
CloudScan - A configuration-free invoice analysis system using recurrent
neural networks | We present CloudScan; an invoice analysis system that requires zero
configuration or upfront annotation. In contrast to previous work, CloudScan
does not rely on templates of invoice layout, instead it learns a single global
model of invoices that naturally generalizes to unseen invoice layouts. The
model is trained using data automatically extracted from end-user provided
feedback. This automatic training data extraction removes the requirement for
users to annotate the data precisely. We describe a recurrent neural network
model that can capture long range context and compare it to a baseline logistic
regression model corresponding to the current CloudScan production system. We
train and evaluate the system on 8 important fields using a dataset of 326,471
invoices. The recurrent neural network and baseline model achieve 0.891 and
0.887 average F1 scores respectively on seen invoice layouts. For the harder
task of unseen invoice layouts, the recurrent neural network model outperforms
the baseline with 0.840 average F1 compared to 0.788.
| 2,017 | Computation and Language |
M2D: Monolog to Dialog Generation for Conversational Story Telling | Storytelling serves many different social functions, e.g. stories are used to
persuade, share troubles, establish shared values, learn social behaviors, and
entertain. Moreover, stories are often told conversationally through dialog,
and previous work suggests that information provided dialogically is more
engaging than when provided in monolog. In this paper, we present algorithms
for converting a deep representation of a story into a dialogic storytelling,
that can vary aspects of the telling, including the personality of the
storytellers. We conduct several experiments to test whether dialogic
storytellings are more engaging, and whether automatically generated variants
in linguistic form that correspond to personality differences can be recognized
in an extended storytelling dialog.
| 2,016 | Computation and Language |
Supervised Speech Separation Based on Deep Learning: An Overview | Speech separation is the task of separating target speech from background
interference. Traditionally, speech separation is studied as a signal
processing problem. A more recent approach formulates speech separation as a
supervised learning problem, where the discriminative patterns of speech,
speakers, and background noise are learned from training data. Over the past
decade, many supervised separation algorithms have been put forward. In
particular, the recent introduction of deep learning to supervised speech
separation has dramatically accelerated progress and boosted separation
performance. This article provides a comprehensive overview of the research on
deep learning based supervised speech separation in the last several years. We
first introduce the background of speech separation and the formulation of
supervised separation. Then we discuss three main components of supervised
separation: learning machines, training targets, and acoustic features. Much of
the overview is on separation algorithms where we review monaural methods,
including speech enhancement (speech-nonspeech separation), speaker separation
(multi-talker separation), and speech dereverberation, as well as
multi-microphone techniques. The important issue of generalization, unique to
supervised learning, is discussed. This overview provides a historical
perspective on how advances are made. In addition, we discuss a number of
conceptual issues, including what constitutes the target source.
| 2,018 | Computation and Language |
SPARQL as a Foreign Language | In the last years, the Linked Data Cloud has achieved a size of more than 100
billion facts pertaining to a multitude of domains. However, accessing this
information has been significantly challenging for lay users. Approaches to
problems such as Question Answering on Linked Data and Link Discovery have
notably played a role in increasing information access. These approaches are
often based on handcrafted and/or statistical models derived from data
observation. Recently, Deep Learning architectures based on Neural Networks
called seq2seq have shown to achieve state-of-the-art results at translating
sequences into sequences. In this direction, we propose Neural SPARQL Machines,
end-to-end deep architectures to translate any natural language expression into
sentences encoding SPARQL queries. Our preliminary results, restricted on
selected DBpedia classes, show that Neural SPARQL Machines are a promising
approach for Question Answering on Linked Data, as they can deal with known
problems such as vocabulary mismatch and perform graph pattern composition.
| 2,017 | Computation and Language |
Revisiting the Centroid-based Method: A Strong Baseline for
Multi-Document Summarization | The centroid-based model for extractive document summarization is a simple
and fast baseline that ranks sentences based on their similarity to a centroid
vector. In this paper, we apply this ranking to possible summaries instead of
sentences and use a simple greedy algorithm to find the best summary.
Furthermore, we show possi- bilities to scale up to larger input docu- ment
collections by selecting a small num- ber of sentences from each document prior
to constructing the summary. Experiments were done on the DUC2004 dataset for
multi-document summarization. We ob- serve a higher performance over the orig-
inal model, on par with more complex state-of-the-art methods.
| 2,017 | Computation and Language |
A dependency look at the reality of constituency | A comment on "Neurophysiological dynamics of phrase-structure building during
sentence processing" by Nelson et al (2017), Proceedings of the National
Academy of Sciences USA 114(18), E3669-E3678.
| 2,018 | Computation and Language |
$k$-Nearest Neighbor Augmented Neural Networks for Text Classification | In recent years, many deep-learning based models are proposed for text
classification. This kind of models well fits the training set from the
statistical point of view. However, it lacks the capacity of utilizing
instance-level information from individual instances in the training set. In
this work, we propose to enhance neural network models by allowing them to
leverage information from $k$-nearest neighbor (kNN) of the input text. Our
model employs a neural network that encodes texts into text embeddings.
Moreover, we also utilize $k$-nearest neighbor of the input text as an external
memory, and utilize it to capture instance-level information from the training
set. The final prediction is made based on features from both the neural
network encoder and the kNN memory. Experimental results on several standard
benchmark datasets show that our model outperforms the baseline model on all
the datasets, and it even beats a very deep neural network model (with 29
layers) in several datasets. Our model also shows superior performance when
training instances are scarce, and when the training set is severely
unbalanced. Our model also leverages techniques such as semi-supervised
training and transfer learning quite well.
| 2,017 | Computation and Language |
Machine Translation in Indian Languages: Challenges and Resolution | English to Indian language machine translation poses the challenge of
structural and morphological divergence. This paper describes English to Indian
language statistical machine translation using pre-ordering and suffix
separation. The pre-ordering uses rules to transfer the structure of the source
sentences prior to training and translation. This syntactic restructuring helps
statistical machine translation to tackle the structural divergence and hence
better translation quality. The suffix separation is used to tackle the
morphological divergence between English and highly agglutinative Indian
languages. We demonstrate that the use of pre-ordering and suffix separation
helps in improving the quality of English to Indian Language machine
translation.
| 2,018 | Computation and Language |
Joint Syntacto-Discourse Parsing and the Syntacto-Discourse Treebank | Discourse parsing has long been treated as a stand-alone problem independent
from constituency or dependency parsing. Most attempts at this problem are
pipelined rather than end-to-end, sophisticated, and not self-contained: they
assume gold-standard text segmentations (Elementary Discourse Units), and use
external parsers for syntactic features. In this paper we propose the first
end-to-end discourse parser that jointly parses in both syntax and discourse
levels, as well as the first syntacto-discourse treebank by integrating the
Penn Treebank with the RST Treebank. Built upon our recent span-based
constituency parser, this joint syntacto-discourse parser requires no
preprocessing whatsoever (such as segmentation or feature extraction), achieves
the state-of-the-art end-to-end discourse parsing accuracy.
| 2,017 | Computation and Language |
Really? Well. Apparently Bootstrapping Improves the Performance of
Sarcasm and Nastiness Classifiers for Online Dialogue | More and more of the information on the web is dialogic, from Facebook
newsfeeds, to forum conversations, to comment threads on news articles. In
contrast to traditional, monologic Natural Language Processing resources such
as news, highly social dialogue is frequent in social media, making it a
challenging context for NLP. This paper tests a bootstrapping method,
originally proposed in a monologic domain, to train classifiers to identify two
different types of subjective language in dialogue: sarcasm and nastiness. We
explore two methods of developing linguistic indicators to be used in a first
level classifier aimed at maximizing precision at the expense of recall. The
best performing classifier for the first phase achieves 54% precision and 38%
recall for sarcastic utterances. We then use general syntactic patterns from
previous work to create more general sarcasm indicators, improving precision to
62% and recall to 52%. To further test the generality of the method, we then
apply it to bootstrapping a classifier for nastiness dialogic acts. Our first
phase, using crowdsourced nasty indicators, achieves 58% precision and 49%
recall, which increases to 75% precision and 62% recall when we bootstrap over
the first level with generalized syntactic patterns.
| 2,017 | Computation and Language |
Generating Different Story Tellings from Semantic Representations of
Narrative | In order to tell stories in different voices for different audiences,
interactive story systems require: (1) a semantic representation of story
structure, and (2) the ability to automatically generate story and dialogue
from this semantic representation using some form of Natural Language
Generation (NLG). However, there has been limited research on methods for
linking story structures to narrative descriptions of scenes and story events.
In this paper we present an automatic method for converting from Scheherazade's
story intention graph, a semantic representation, to the input required by the
Personage NLG engine. Using 36 Aesop Fables distributed in DramaBank, a
collection of story encodings, we train translation rules on one story and then
test these rules by generating text for the remaining 35. The results are
measured in terms of the string similarity metrics Levenshtein Distance and
BLEU score. The results show that we can generate the 35 stories with correct
content: the test set stories on average are close to the output of the
Scheherazade realizer, which was customized to this semantic representation. We
provide some examples of story variations generated by personage. In future
work, we will experiment with measuring the quality of the same stories
generated in different voices, and with techniques for making storytelling
interactive.
| 2,017 | Computation and Language |
Identifying Subjective and Figurative Language in Online Dialogue | More and more of the information on the web is dialogic, from Facebook
newsfeeds, to forum conversations, to comment threads on news articles. In
contrast to traditional, monologic resources such as news, highly social
dialogue is very frequent in social media. We aim to automatically identify
sarcastic and nasty utterances in unannotated online dialogue, extending a
bootstrapping method previously applied to the classification of monologic
subjective sentences in Riloff and Weibe 2003. We have adapted the method to
fit the sarcastic and nasty dialogic domain. Our method is as follows: 1)
Explore methods for identifying sarcastic and nasty cue words and phrases in
dialogues; 2) Use the learned cues to train a sarcastic (nasty) Cue-Based
Classifier; 3) Learn general syntactic extraction patterns from the sarcastic
(nasty) utterances and define fine-tuned sarcastic patterns to create a
Pattern-Based Classifier; 4) Combine both Cue-Based and fine-tuned
Pattern-Based Classifiers to maximize precision at the expense of recall and
test on unannotated utterances.
| 2,017 | Computation and Language |
Generating Sentence Planning Variations for Story Telling | There has been a recent explosion in applications for dialogue interaction
ranging from direction-giving and tourist information to interactive story
systems. Yet the natural language generation (NLG) component for many of these
systems remains largely handcrafted. This limitation greatly restricts the
range of applications; it also means that it is impossible to take advantage of
recent work in expressive and statistical language generation that can
dynamically and automatically produce a large number of variations of given
content. We propose that a solution to this problem lies in new methods for
developing language generation resources. We describe the ES-Translator, a
computational language generator that has previously been applied only to
fables, and quantitatively evaluate the domain independence of the EST by
applying it to personal narratives from weblogs. We then take advantage of
recent work on language generation to create a parameterized sentence planner
for story generation that provides aggregation operations, variations in
discourse and in point of view. Finally, we present a user evaluation of
different personal narrative retellings.
| 2,017 | Computation and Language |
Narrative Variations in a Virtual Storyteller | Research on storytelling over the last 100 years has distinguished at least
two levels of narrative representation (1) story, or fabula; and (2) discourse,
or sujhet. We use this distinction to create Fabula Tales, a computational
framework for a virtual storyteller that can tell the same story in different
ways through the implementation of general narratological variations, such as
varying direct vs. indirect speech, character voice (style), point of view, and
focalization. A strength of our computational framework is that it is based on
very general methods for re-using existing story content, either from fables or
from personal narratives collected from blogs. We first explain how a simple
annotation tool allows naive annotators to easily create a deep representation
of fabula called a story intention graph, and show how we use this
representation to generate story tellings automatically. Then we present
results of two studies testing our narratological parameters, and showing that
different tellings affect the reader's perception of the story and characters.
| 2,017 | Computation and Language |
Comparing Human and Machine Errors in Conversational Speech
Transcription | Recent work in automatic recognition of conversational telephone speech (CTS)
has achieved accuracy levels comparable to human transcribers, although there
is some debate how to precisely quantify human performance on this task, using
the NIST 2000 CTS evaluation set. This raises the question what systematic
differences, if any, may be found differentiating human from machine
transcription errors. In this paper we approach this question by comparing the
output of our most accurate CTS recognition system to that of a standard speech
transcription vendor pipeline. We find that the most frequent substitution,
deletion and insertion error types of both outputs show a high degree of
overlap. The only notable exception is that the automatic recognizer tends to
confuse filled pauses ("uh") and backchannel acknowledgments ("uhhuh"). Humans
tend not to make this error, presumably due to the distinctive and opposing
pragmatic functions attached to these words. Furthermore, we quantify the
correlation between human and machine errors at the speaker level, and
investigate the effect of speaker overlap between training and test data.
Finally, we report on an informal "Turing test" asking humans to discriminate
between automatic and human transcription error cases.
| 2,017 | Computation and Language |
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