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train_11100 | Even with these additions and transformations, there are still too few training data available to train a good translation model. | we think the grammar error correction system should 1) correct most kinds of errors in a unified framework and 2) use as much unlabeled data as possible instead of using large amount of human annotated data. | contrasting |
train_11101 | We select feedback of varying grade by directly inspecting the optimal w * , thus this feedback is idealized. | the experiment also has a realistic background since we show that α-informative feedback corresponds to improvements under standard evaluation metrics such as lowercased and tokenized TER, and that learning from weak and strong feedback leads to convergence in TER on test data. | contrasting |
train_11102 | In coreference resolution, a fair amount of research treats mention detection as a preprocessed step and focuses on developing algorithms for clustering coreferred mentions. | there are significant gaps between the performance on gold mentions and the performance on the real problem, when mentions are predicted from raw text via an imperfect Mention Detection (MD) module. | contrasting |
train_11103 | Standard methods define mentions as boundaries of text, and expect exact boundaries as input in the coreference step. | mentions have an intrinsic structure, in which mention heads carry the crucial information. | contrasting |
train_11104 | 16 The mention head candidate generation module has a bigger impact on MDER compared to the joint framework. | they both have the same level of positive effects on PGR for coreference resolution. | contrasting |
train_11105 | Cardie and Pierce (1998) propose to select certain rules based on a given corpus, to identify base noun phrases. | the phrases detected are not necessarily mentions that we need to discover. | contrasting |
train_11106 | CCM is able to learn which POS contexts are likely, and does so via a probabilistic generative model, providing a statistical, data-driven take on substitutability. | since there is nothing intrinsic about the POS pair DET-VERB that indicates a priori that it is a likely constituent context, this fact must be inferred entirely from the data. | contrasting |
train_11107 | (2007) to sample parse trees for sentences in the raw training corpus according to their posterior probabilities. | due to the very large sets of potential supertags used in a parse, computing inside charts is intractable, so we design a Metropolis-Hastings step that allows us to sample efficiently from the correct posterior. | contrasting |
train_11108 | MSA is used in formal settings, edited media, and education. | the spoken, and, currently written in social media and penetrating formal media, are the informal vernaculars. | contrasting |
train_11109 | The misclassified word in the first examples (bED meaning "each other") has a gold class other. | the gold label is incorrect and our system predicted it correctly as lang2 given the context. | contrasting |
train_11110 | In the 10-fold-cross-validation on the sentTrnDB using Comp-Cl alone, we note that performance results slightly decreased (from 87.3% to 87.0%). | given the sparsity of the feature (it occurs in less than 1% of the tokens in the EDA sentences), 0.3% drop in performance is significant. | contrasting |
train_11111 | Training models from scratch for every new domain requires human annotated labeled data which is expensive and time consuming, hence, not pragmatic. | transfer learning techniques allow domains, tasks, and distributions used in training and testing to be different, but related. | contrasting |
train_11112 | If θ 1 is low, C t may get trained on incorrectly predicted pseudo labeled instances; whereas, if θ 1 is high, C t may be deficient of instances to learn a good decision boundary. | θ 2 influences the number of iterations required by the algorithm to reach the Figure 4: Illustrates how the weight (w t ) for target domain classifiers varies for the most and least similar domains with number of iterations. | contrasting |
train_11113 | SCL based domain adaptation does not yields generous improvements as selecting the pivot features and computing the cooccurrence statistics with noisy short text is arduous and inept. | the proposed algorithm iteratively learns discriminative target specific features from such perplexing data and translates it to an improvement of at least 6.4% and 3.5% over the baseline and the SCL respec- Figure 7: Results comparing the accuracy of the proposed approach with existing techniques for cross domain categorization on the real world dataset. | contrasting |
train_11114 | We observed that MERT learns the verbosity of the tuning dataset very well, but this can be a disadvantage because we do not know the verbosity of unseen test sentences. | pRO is affected by both the verbosity and the source-side length of the tuning dataset. | contrasting |
train_11115 | For each word that appeared in the dependency relation arcs, we can use the number of appearances of its interlingual features as the corresponding feature values. | the sentences in the target language are not fully labeled. | contrasting |
train_11116 | That is, it estimates the probability of each w given its local context. | sKIP-GRAM applies softmax to each context word of a given occurrence of word w. In this case, v ctx(w) corresponds to the representation of one of its context words. | contrasting |
train_11117 | If we treat word representations as fixed, the graph transformer is a simple linear-chain CRF. | if we can treat the word representations as model parameters, the model is equivalent to a neural network with word embeddings as the input layer, as shown in Figure 1. | contrasting |
train_11118 | Another difference is that we tuned the hyperparameters with random search, to enable replication using the same random seed. | the hyperparameters for the state-of-the-art methods are tuned more extensively by experts, making them more difficult to reproduce. | contrasting |
train_11119 | This paper addresses the issue systematically on a large scale. | to previous work in both sociolinguistics and NLP, we consider syntactic variation across groups at the level of treelets, as defined by dependency struc-tures, and make use of a large corpus that includes demographic information on both age and gender. | contrasting |
train_11120 | In principle, we could directly check for significant differences in the demographic groups and use Bonferroni correction to control the family-wise error (i.e., the probability of obtaining a false positive). | given the large number of treelets, the correction for multiple comparisons would underpower our analyses and potentially cause us to miss many significant differences. | contrasting |
train_11121 | This approach restricts the findings to the phenomena defined in the hypothesis, in this case the word list used. | our approach works beyond the lexical level, is data-driven and thus unconstrained by prior hypotheses. | contrasting |
train_11122 | Existing studies on variation have thus mostly focused on lexical and phonological variation. | we study the effect of age and gender on syntactic variation across several languages. | contrasting |
train_11123 | Although this avoids the need for a target language treebank, most approaches have still used large parallel corpora. | parallel data is scarce for low-resource languages, and we report a new method that does not need parallel data. | contrasting |
train_11124 | Supervised approaches to dependency parsing have been very successful for many resource-rich languages, where relatively large treebanks are available (McDonald et al., 2005a). | for many languages, annotated treebanks are not available, and are very costly to create (Böhmová et al., 2001). | contrasting |
train_11125 | Given a source-language parse tree along with word alignments, they generate the targetlanguage parse tree by projection. | their approach relies on many heuristics which would be difficult to adapt to other languages. | contrasting |
train_11126 | In summary, existing work generally starts with a delexicalized parser, and uses parallel data typological information to improve it. | we want to improve the delexicalized parser, but without using parallel data or any explicit linguistic resources. | contrasting |
train_11127 | The experimental results presented in Figure 2 correspond to these findings: we can see improvements in the neural network performance when increasing the word embeddings dimensionality from 50 to 100 and from 100 to 200. | the Ask Ubuntu data set containing approximately 121M tokens is not big enough for an improvement when increasing the dimensionality from 200 to 400. | contrasting |
train_11128 | The performance of an SVM-based approach to this task was shown to depend highly on the size of the training data. | the CNN with in-domain word embeddings provides very high performance even with limited training data. | contrasting |
train_11129 | Because |C| is the number of hidden-to-hidden connection types, we cannot apply caching to reduce this term. | |C| is much smaller than |F| (here |C|=3). | contrasting |
train_11130 | The structure follows the proposed pipeline approach by Klinger and Cimiano (2013). | 1 in contrast to their work, we focus on the detection of phrases only, and exploit the detection of relations only during inference, such that the detection of relations has an effect on the detection of phrases, but is not evaluated directly. | contrasting |
train_11131 | We use exact match precision, recall and F 1 -measure for evaluation. | it should be noted that partial matching scores are also commonly applied in fine-grained sentiment analysis due to the fact that boundaries of annotations can differ substantially between annotators. | contrasting |
train_11132 | Our approach relying on instance filtering comes close to results produced by a system trained on manually annotated data for the target language on the task of predicting aspect phrases. | to these results for the aspect phrase recognition, the impact of filtering training instances is negligible for the detection of subjective phrases. | contrasting |
train_11133 | This can as well be observed in the number of predictions the models based on different thresholds generate: While the number of true positive aspects for the coffee machine subdomain is 1100, only 221 are predicted with a threshold of the manual quality assignment of 0. | a treshold of 9 leads to 560 predictions and a threshold of 10 to 1291. | contrasting |
train_11134 | For the fusional languages (English, German and Indonesian) we see modest gains in performance on both root detection and stemming. | for the agglutinative languages (Finnish, Turkish and Zulu) we see absolute gains as high as 50% 10 Thus in our experiments there are no stem alternations. | contrasting |
train_11135 | Finite-state morphological analyzers output a small set of linguistically valid analyses of a type, typically with only limited overgeneration. | there are two significant problems. | contrasting |
train_11136 | 14 In fact, this is usually solved by viewing it as a different problem, morphological guessing, where linguistic knowledge similar to the features we have presented is used to try to guess POS and morphological analysis for types with no analysis. | our training procedure learns a probabilistic transducer, which is a soft version of the type of hand-engineered grammar that is used in finite-state analyzers. | contrasting |
train_11137 | One may consider connecting multiple resource graphs at the term nodes. | this may cause sense-shifts, i.e. | contrasting |
train_11138 | The generative models (CSLDA and MOMRESP) tend to excel in lowannotation portions of the learning curve, partially because generative models tend to converge quickly and partially because generative models naturally learn from unlabeled documents (i.e., semi-supervision). | mOmRESP tends to quickly reach a performance plateau after which additional annotations do little good. | contrasting |
train_11139 | When data clusters and label classes are misaligned, MOMRESP falters (as in the case of the Cade12 dataset). | cSLDA's flexible mapping from topics to labels is less sensitive: topics can diverge from label classes so long as there exists some linear transformation from the topics to the labels. | contrasting |
train_11140 | (2014) adapted CBOW (Mikolov et al., 2013a) to train word embeddings on different datasets: free text documents from Wikipedia, search click-through data and user query data, showing that combining them gets stronger results than using individual word embeddings in web search ranking and word similarity task. | these two papers either learned word representations on the same corpus (Turian et al., 2010) or enhanced the embedding quality by extending training corpora, not learning algorithms (Luo et al., 2014). | contrasting |
train_11141 | 5 The table also shows that WordNet may not be appropriate for our present verb categorization task. | it may be suitable for other subtasks in sentiment analysis, particularly polarity classification. | contrasting |
train_11142 | The relatively strong naming performance of G can be attributed to the fact that many demonstrations had similarities among the objects presented that could be learned from choosing any of the objects. | reference resolution performance for G averaged a .34 F1-score compared with a .70 F1-score for our best performing configuration. | contrasting |
train_11143 | Indeed, when evaluating these VSMs with datasets such as wordsim353 (Finkelstein et al., 2001), where the word pair scores re-flect association rather than similarity (and therefore the (cup,coffee) pair is scored higher than the (car,train) pair), the Spearman correlation between their scores and the human scores often crosses the 0.7 level. | when evaluating with datasets such as SimLex999 (Hill et al., 2014), where the pair scores reflect similarity, the correlation of these models with human judgment is below 0.5 (Section 6). | contrasting |
train_11144 | For example, in word classification tasks, words such as "big" and "small" potentially belong to the same class (size adjectives), and thus representing them as similar is desired. | antonyms are very dissimilar by definition. | contrasting |
train_11145 | The table shows that when the antonym parameter is off, our model generally recognizes antonyms as similar. | when the parameter is on, ranks of antonyms substantially decrease. | contrasting |
train_11146 | The feature vector g n ∈ R 2d×1 is the concatenation of N(n 1 ) and N(n 2 ): Words between the noun pair contribute to classifying the relation, and one of the most common ways to incorporate an arbitrary number of words is treating them as a bag of words. | word order information is lost for bag-of-words features such as averaged word embeddings. | contrasting |
train_11147 | The main advantage of such models is that they allow a large family of rich features that include dependency features, constituent features and conjunctions of the two. | the consequence is that the additional spinal structure greatly increases the number of dependency relations. | contrasting |
train_11148 | In this case, to produce a constituent tree from the spinal forest, we promote the last tree and place the rest of trees as children of its top node. | in this section we describe the performance of the transition-based spinal parser by running it with different sizes of the beam and by comparing it 2 this is not always the case. | contrasting |
train_11149 | However, unlike , the arc-eager parser does not substantially benefit of using the triplets during training. | our best model (obtained with beam=64) provides 92.14 UAS, 90.91 TA and 89.32 TAS in the test set including punctuation and 92.78 UAS, 91.53 6 in absolute terms, our running times are slower than typical shift-reduce parsers. | contrasting |
train_11150 | In that work dependency labels encode the constituent node where the dependency arises as well as the position index of that node in the head spine. | we use constituent triplets as dependency labels. | contrasting |
train_11151 | The Accuracy column shows the cross-validation accuracy, and the Diff column shows the improvement over the majority class baseline. | this is partly due to the increased class imbalance of trolls vs. non-trolls, which can be seen by the decrease in the improvement of our classifier compared to the majority class baseline. | contrasting |
train_11152 | Feature selection techniques for traditional documents have been aplenty and a few seminal survey articles have been written on this topic (Blitzer, 2008). | for short text there is much less work on statistical feature selection but more focus has gone to feature engineering towards word normalization, canonicalization etc. | contrasting |
train_11153 | In supervised learning, we can estimate the accuracy of a model on a subset of the labeled data and choose the model with the highest accuracy. | here we focus on type-supervised learning, which uses constraints over the possible labels for word types for supervision, and labeled data is either not available or very small. | contrasting |
train_11154 | Fully supervised training of NLP models (e.g., part-of-speech taggers, named entity recognizers, relation extractors) works well when plenty of labeled examples are available. | manually labeled corpora are expensive to construct in many languages and domains, whereas an alternative, if weaker, supervision is often readily available. | contrasting |
train_11155 | Using the 300 labeled sentences for semi-supervised training and model selection reduced the error by 44.6% (comparing to the model with best average accuracy using only type-level supervision with average performance of 85.05, the semi-supervised average is 91.8). | using the 300 sentences to select hyperparameters only reduced the error by less than 5% (the average accuracy was 85.75). | contrasting |
train_11156 | Some researchers believe that, in some cases, WSI methods may perform better than WSD systems (Jurgens and Klapaftis, 2013;Wang et al., 2015). | we argue that WSI systems have few advantages compared to WSD methods and according to our results, disambiguation systems consistently outperform induction systems. | contrasting |
train_11157 | We found that only 4% of errors are caused by wrong sentence or word alignment. | 69% of erroneous sense-tagged instances are the result of a Chinese word associated with multiple senses of a target English word. | contrasting |
train_11158 | We notice that the DSO corpus generally improves the performance of our system. | since the annotated DSO corpus is copyrighted, we are unable to release a dataset including the DSO corpus. | contrasting |
train_11159 | Since the MUN dataset does not cover all target word types in the all-words shared tasks, the accuracy achieved with MUN alone is lower than the SC and SC+DSO settings. | the evaluation results show that IMS trained on MUN alone often performs better than or is competitive with the WordNet Sense 1 baseline. | contrasting |
train_11160 | assumptions, and makes it capable of modeling complex distributions over sequences, including those with long-term dependencies. | by breaking the model structure down into a series of next-step predictions, the rnnlm does not expose an interpretable representation of global features like topic or of high-level syntactic properties. | contrasting |
train_11161 | When labeled by SU-Time 1 or HeidelTime 2 , the adverb currently is correctly tagged with the PRESENT_REF value. | if we change the sentence to "Apple's iPhone is one of the most popular smartphones at the present day", no temporal mention is found, although one may expect that within this context currently and present day share some equivalent temporal dimension. | contrasting |
train_11162 | Re-sults show that TRMC2 outperforms all other approaches and achieves highest performance in terms of precision, recall, and F1-measure. | more important still is the fact that a simple learning strategy with some temporal lexicon (MC2 or TWnH) leads to improved results, when compared to some solution that does not take advantage of such a resource (UTTime, here). | contrasting |
train_11163 | For example, if we know the place of birth and/or the place of death of a person and/or the location where the person is living, it is likely that we can predict the person's nationality. | if we know that a person works for an organization and that this person is also the top member of that organization, then it is possible that this person is the CEO of that organization. | contrasting |
train_11164 | Unlike the case of single-word contexts, it is not feasible to explicitly compute here this PMI matrix due to the exponential number of possible sentential contexts. | the objective function that we optimize still aims to best approximate it. | contrasting |
train_11165 | We have investigated the impact of corrective feedback on first language acquisition, in particular on the reduction of subject omission errors in English-a type of error which we found to be the most commonly met with CF in our corpus study. | to previous small-scale studies in psycholinguistics, we have addressed this problem using a comparatively large data-driven setting. | contrasting |
train_11166 | A similar project by Labutov and Lipson (2014) likewise considers the effect of context on guessing the L2 word. | it does not consider the effect of the L2 word's spelling, which we show is also important. | contrasting |
train_11167 | We train a log-linear model to predict the words that our subjects guess on training data, and we will check its success at this on test data. | from an engineering perspective, we do not actually need to predict the user's specific good or bad answers, but only whether they are good or bad. | contrasting |
train_11168 | As the empirical guessability increases, so does the median model probability assigned to the correct answer. | in our applications, we are less interested in only the 1-best prediction; we'd like to know whether users can understand the novel vocabulary, so we'd prefer to allow WordNet synonyms to also be counted as correct. | contrasting |
train_11169 | The URLL distance between this tree and the gold standard in Figure 1 is 0.12. | 10 the MDL costs do not allow us to prefer any one of the context models over the others. | contrasting |
train_11170 | When classification techniques are used, we obtain the best F-score of 79.8% with SVM (O). | when sequence labeling techniques are used, the best F-score is 84.2%. | contrasting |
train_11171 | This means that our intuition that sequence labeling will better capture conversational context reflects in the forms of sarcasm for which sequence labeling improves over classification. | examples where our system makes errors can be grouped as: • Topic Drift: Eisterhold et al. | contrasting |
train_11172 | (2016) for modeling sarcasm understandability of readers. | as far as we know, these features are being introduced in NLP tasks like sentiment analysis for the first time. | contrasting |
train_11173 | Recent work in sarcasm detection on social media has tried to incorporate contextual information by exploiting the preceding messages of a user, to e.g., detect contrasts in sentiments expressed towards named entities (Khattri et al., 2015), infer behavioural traits (Rajadesingan et al., 2015) and capture the relationship between authors and the audience (Bamman and Smith, 2015). | all of these approaches require the design and implementation of complex features that explicitly encode the content and (relevant) context of messages to be classified. | contrasting |
train_11174 | To train our neural model, we first had to choose a suitable architecture and hyperparameter set. | selecting the optimal network parametrization would require an extensive search over a large configuration space. | contrasting |
train_11175 | Clearly projections contain useful information, as the tagging accuracy is well above chance. | they are riddled with noise and biases, which need to be accounted for to improve performance. | contrasting |
train_11176 | As a preprocessing step, pseudo-projectivization of the syntactic trees (Nivre et al., 2007) was used, which allowed an accurate conversion of even the non-projective syntactic trees into syntactic transitions. | the oracle conversion of semantic parses into transitions is not perfect despite using the M-SWAP action, due to the presence of multiple crossing arcs. | contrasting |
train_11177 | This setup bears similarity to other approaches which pipeline syntax and semantics, extracting features from the syntactic parse to help SRL. | unlike other approaches, this model does not offer the entire syntactic tree for feature extraction, since only the partial syntactic structures present on the syntactic stack (and potentially the buffer) are visible at a given timestep. | contrasting |
train_11178 | (2015) proposed an efficient joint model for CCG syntax and SRL, which performs better than a pipelined model. | their training necessitates CCG annotation, ours does not. | contrasting |
train_11179 | This can be a reasonable assumption in text or speech transcription (Toselli et al., 2007;Rodríguez et al., 2007) where the output sequence is generated monotonically respect to the input data. | it has always be an important handicap for translation due to the intrinsic reordering involved in the process. | contrasting |
train_11180 | Obviously, it does not qualify as real-time. | we expect an important reduction in response time after implementing our approach in a more efficient language. | contrasting |
train_11181 | This reduction in typing effort came at the expense of a larger amount of mouse actions required to validate correct segments of the suggested translations. | since mouse actions are cheaper than typing full words, we can expect this exchange to reduce overall user effort. | contrasting |
train_11182 | Traditionally, these models provide point estimates and are evaluated using metrics like Mean Absolute Error (MAE), Root-Mean-Square Error (RMSE) and Pearson's r correlation coefficient. | in practice QE models are built for use in decision making in large workflows involving Machine Translation (MT). | contrasting |
train_11183 | Most kernel methods tie all lengthscale to a single value, resulting in an isotropic kernel. | since in GPs hyperparameter optimisation can be done efficiently, it is common to employ one lengthscale per feature, a method called Automatic Relevance Determination (ARD). | contrasting |
train_11184 | Intuitively, if two models produce equally incorrect predictions but they have different uncertainty estimates, NLPD will penalise the overconfident model more than the underconfident one. | if predictions are close to the true value then NLPD will penalise the underconfident model instead. | contrasting |
train_11185 | That is, a named entity recognizer is first applied to identify mentions of interest, and then a wikifier is used to ground the extracted mentions to Wikipedia entries. | to this traditional pipeline, we show that the ability to ground and disambiguate words is very useful to NER. | contrasting |
train_11186 | They do NER using Wikipedia category features for each mention. | their method for wikifying text is not robust to ambiguity, and they only do monolingual NER. | contrasting |
train_11187 | If it fails, we find the corresponding English Wikipedia title via interlanguage links and then query the API with the English title. | freeBase does not contain entities in Yoruba, Bengali, and Tamil, so the first step will always fail for these three languages. | contrasting |
train_11188 | (2015) turn to convolution network directly based on the original word2vec (Mikolov et al., 2013a). | they pay little attention to design effective word and entity representations. | contrasting |
train_11189 | This feature is directly counted from Wikipedia's anchor fields and measures the link probability of an entity e given a mention m. Prior is a strong indicator (Fader et al., 2009) to select the correct entity. | it is unwise to take prior as a feature all the time because prior usually get a very large weight, which overfits the training data. | contrasting |
train_11190 | The best performing solutions are supervised, discriminative learning methods which learn transliteration models from parallel transliteration corpora. | such corpora are available only for some language pairs. | contrasting |
train_11191 | If the switch is turned on, the decoder produces a word from its target vocabulary in the normal fashion. | if the switch is turned off, the decoder instead generates a pointer to one of the word-positions in the source. | contrasting |
train_11192 | Although this dataset is smaller and more complex than the Gigaword corpus, it is interesting to note that the Rouge numbers are in the same range. | our switching pointer/generator model as well as the hierarchical attention model described in Sec. | contrasting |
train_11193 | This implementation has the advantage of simplicity as it requires minimal changes to the training and deployment code, but we note that a more complex implementation utilizing sparse matrices and sparse matrix multiplication could potentially yield speed improvements. | such an implementation is beyond the scope of this paper. | contrasting |
train_11194 | Indeed these senses are presupposed by listeners according to linguistics theories (Segal et al., 1991;Murray, 1997;Levinson, 2000;Sanders, 2005;Kuperberg et al., 2011). | asr and Demberg (2015) finds that DCs are more often dropped for the discourse relation Chosen alternative (the relation typically signalled by the DC 'instead'), if the context contains negation words, which are identified cues for this relation. | contrasting |
train_11195 | Asr and Demberg (2012; 2015) attribute the corpus statistics to the UID hypothesis, which explains that expected, predictable relations are more likely to be conveyed implicitly, and thus more ambiguously, to maintain steady information flow. | there are explicit 'causal' and 'continuous' relations and some Chosen Alternative are marked even argument 1 is negated. | contrasting |
train_11196 | e U (imp;s,C) = e U (null;s,C) + e U (args;s,C) (9) The amount of information that the null DC provides for the discourse relation is defined similarly as in Equation (8): On the other hand, the informativeness of arguments, I(s; arg, C) is quantified by negative surprisal in RSA. | arguments are clauses and sentences. | contrasting |
train_11197 | We also plan to evaluate the effectiveness of the model in applications, such as natural language generation or machine translation tasks. | as discourse presentation differs across genres (Webber, 2009) and mediums (Tonelli et al., 2010), the model can be applied to predict the explicitation of discourse relations from, for example, news articles to spoken dialogues. | contrasting |
train_11198 | In (Axelrod et al., 2011;Duh et al., 2013), the sizes of the in-domain data sets are 30K and over 100K sentences respectively. | we do not always have access to large or even medium amounts of in-domain data. | contrasting |
train_11199 | For example, in "I have a Dell desktop and a Macbook laptop", the words "Dell, laptop, Macbook, laptop" are from the computer domain, while the words "I, have, a, and" are general. | the topic of this sentence is decided by the domain specific words, not the general-domain words. | contrasting |
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