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LanguageTechnology | How to improve your video editing software with language technology AI API? | nan | 0.67 | t3_u2lkg2 | 1,649,838,411 |
LanguageTechnology | Translation/localization market research report out for 2022 | The 2022 market report on translation, localization, and language services is out. Credit Nimdzi Insights. [http://mh.nimdzi.com/l10n\_research\_2022](http://mh.nimdzi.com/l10n_research_2022) | 1 | t3_u2k9g3 | 1,649,832,594 |
LanguageTechnology | Fine-Tuning LayoutLM v2 For Invoice Recognition | With the advent of deep learning models, automated data extraction is becoming more accessible. [In this article](https://towardsdatascience.com/fine-tuning-layoutlm-v2-for-invoice-recognition-91bf2546b19e), we demonstrate step-by-step how to fine-tune layoutLM V2 on invoices starting from data annotation to model training and inference.
Enjoy the read and if you have any questions, leave them below. | 0.95 | t3_u28bcc | 1,649,794,508 |
LanguageTechnology | [D] Expert Advice is needed on designing a feedback Loop for a (Textual Classification + NER) task in Production. | nan | 1 | t3_u1v85f | 1,649,756,830 |
LanguageTechnology | Subword Segmentation Algorithm | nan | 1 | t3_u1u300 | 1,649,751,865 |
LanguageTechnology | Looking for Professors/Experts in the field of NLP/ML with some works on the Arabic Language | Hi there,
I am currently doing a Ph.D. in Computational Linguistics at Korea Advanced Institute of Science and Technology (KAIST), South Korea. I have reached the final stage of my Ph.D. and currently preparing to do my thesis proposal.
The research within my thesis takes an attempt at modeling the Arabic dialects and brings them up to date with recent NLP breakthroughs such as the transformer-based language models. My advisor asked me to find a member with some background in the Arabic NLP field to be part of my thesis committee. I have contacted some Professors that I could find online but received no answers as of yet. So, I would really appreciate it if any of you could put me in touch with a professor or an expert in this field.
The proposal (scheduled to be in the next two weeks) and defense (2 months after the proposal) are going to be online and will only take about an hour each to complete.
Thank you for your help and have a great day. | 1 | t3_u1tlmg | 1,649,749,746 |
LanguageTechnology | Word Mover's distance between 2 documents | I'm trying to compute the word mover's distance (a variant of earthmover distance) between two documents. I want to represent any transformer-based model to generate document embeddings. Is there any resource/code-snippet I should look into? | 0.5 | t3_u1spht | 1,649,745,930 |
LanguageTechnology | Entity extractor | I am trying to build an entity extractor that can extract the shop number from the full shop name (e.g. McDonalds F1287, result should be 1287). I tried making a custom spacy ner but it does not work at all (ent.text and ent.label are blank). How can I go about doing this? Does this need DNNs? LSTM, CNN? Or transformer models? | 0.5 | t3_u1rino | 1,649,742,539 |
LanguageTechnology | Curriculum to learn NLP | I'm planning on self-studying natural language processing. I'm wondering if someone can create an outline of a curriculum that I can follow. Something like prerequisites before studying NLP, like what math and linguistics concepts I should know. I am already confident in programming and can learn Python easily. | 1 | t3_u1pja3 | 1,649,733,947 |
LanguageTechnology | Master Program in LT in Gothenburg or Potsdam | Hello!
I'm looking for a piece of advice where to do my Master's. I'm choosing between Cognitive Systems at Potsdam and LT at Gothenburg. I have a background in linguistics as my major and data science as my minor so I have enough programming background. I'm interested in research facilities and math courses. What should I choose?
Thank you for your answers | 1 | t3_u1lzho | 1,649,723,240 |
LanguageTechnology | Should I go BERT? | Should I go BERT if I am just looking for tokenization, POS tagging? I know it is the state of the art way of doing NLP, but given I just need to execute those relatively simple tasks, should I go BERT or something easier? | 1 | t3_u1jyfw | 1,649,717,319 |
LanguageTechnology | ClinicalBERT model for Medical NLP Tasks | Hi,
I am using the Clinical BERT model to de id the i2b2 dataset. However, this is the first time I am dealing with all this data science and code, and do not know how to go about doing this. Does anyone here have experience in using these Domain-Specific BERT models? If so, any help would be greatly appreciated! | 1 | t3_u1h9vo | 1,649,710,301 |
LanguageTechnology | Question about text classification |
Good afternoon,
I want to do a comparative study that adds new in this area
(classification of documents according to their subject),
I chose these families to make the comparison:
{GNN : taxteGcn , SGC }
{trans : bert , roberta}
{GNN With trans : bertGCN, robertaGcn,BertMp-GCn}
but the problem if I do this comparison at this time
I will redo a job that exists, according to the articles
the majority of comparisons compare Accuracy.
(for me what I am interested in is performance and time too)
My question: how can we avoid redoing a comparison that exists
in order to add something new between the 3 families that I have chosen
always in the field of classification of documents according to their subjects
or what do you think to do in this case?
Warm Regards. | 1 | t3_u185f1 | 1,649,684,802 |
LanguageTechnology | How do you deploy ML models in production (Kubernetes) for scalability? | I’m currently looking into scalable ways to deploy machine learning models (esp. Huggingface models like T5) in a CPU-only production (on-premise) environment.
There are many different tutorials and guides available online, however almost all of them only scratch the surface and basically describe how you load a model and wrap it as it is in a Flask / Fast API application, dockerize it and deploy it into a Kubernetes cluster to quickly get it accessible through an API.
In my experience this works as long as you have a very low number of requests. As soon as the number of (simultaneous) requests increases, this approach runs into multiple problems like pods running Out-Of-Memory due to each requests increasing the memory requirements by the pod by a certain amount (e.g. mt5 requires \~3GB memory when idling. This grows by X00MB for each request that is forwarded through the model. If this exceeds the available memory for the pod, the pod crashes with OOM) etc.
Also, due to the large size of ML containers, one of the main advantages of Kubernetes - horizontal scaling - is also tricky to achieve due to the lag between initialising the new pod and it being ready to use. Even if this would work well, you still run into the OOM errors described above.
Of course, one can always try to optimise models by distilling or pruning their models, deploy them using ONNX or apply queuing mechanisms to handle incoming requests sequentially, however this only treats the symptoms and does not solve the underlying challenge.
What are commonly used ways to overcome this shortcomings? Are there best practices? | 0.92 | t3_u17o3f | 1,649,683,383 |
LanguageTechnology | How to extract keyword from multilingual texts | nan | 1 | t3_u17dyw | 1,649,682,529 |
LanguageTechnology | Frameworks for bringing together NLP and enterprise document management systems? | From my perspective, applying NLP tools to documents on a larger scale requires frameworks that bring together document management systems and context-sensitive NLP functions that can be customized based on user-defined business rules.
In other words: A (i) document management system plus a (ii) rule-engine that serves as an interface to dynamically include customer-specific NLP functions into the automated processing pipeline for documents.
There are some proprietary closed-sourced systems on the market. But I wonder what open source frameworks / open standards do exist to define such customer-specific rules and NLP tasks.
I am not aware of any active community digging into these questions. Would be happy to get some references here. | 1 | t3_u15nuu | 1,649,676,972 |
LanguageTechnology | Where can I learn about style transfer in text? | I'm interested in improving my writing by making a model to give suggestions on writing style. I've heard that there is work in style transfer for text. Where can I learn this, from the beginner's perspective to implementation? Thanks | 1 | t3_u0mqg9 | 1,649,612,598 |
LanguageTechnology | How to change BERT pre-training tasks on HuggingFace? | Hello all!
I am fairly new to HuggingFace, and have only used the most famous functionalities as of now and am now looking into how to make my own BERT model. I found a few blogs which detail the procedure to train a model from scratch and some to further pre-train/finetune on a custom dataset. What I am unable to find is "How to pre-train a BERT model (say RoBERTa) on different pre-training tasks (say MLM+RTD) using HuggingFace?" Is it even possible? I would appreciate all and any help regarding my query.
Thanks! | 0.92 | t3_u0ei3v | 1,649,584,691 |
LanguageTechnology | Check Out This DeepMind’s New Language Model, Chinchilla (70B Parameters), Which Significantly Outperforms Gopher (280B) and GPT-3 (175B) on a Large Range of Downstream Evaluation Tasks | Extreme-scale language models have recently exhibited incredible performance on natural language processing challenges. This is due to their ever-increasing size, exceeding 500 billion parameters. However, while these models have grown in popularity in recent years, the amount of data utilized to train them has not increased. The current generation of huge language models is clearly undertrained. Three prediction approaches for optimally choosing both model size and training length have been proposed by a DeepMind research team.
Three approaches have been mentioned to estimate the optimal parameter:
* Change the size of the models and the number of training tokens.
* IsoFLOP profiles
* Using a parametric loss function to fit a model
The ultimate pretraining loss is calculated as the number of model parameters and training tokens. They minimize the loss function under the restriction of the FLOPs function, which is equal to the computational budget because the computational budget is a probabilistic function of the number of observed training tokens and model parameters.
[Continue Reading This Research Summary](https://www.marktechpost.com/2022/04/09/check-out-this-deepminds-new-language-model-chinchilla-70b-parameters-which-significantly-outperforms-gopher-280b-and-gpt-3-175b-on-a-large-range-of-downstream-evaluation-tasks/)
Paper: https://arxiv.org/pdf/2203.15556.pdf | 0.96 | t3_tzzor1 | 1,649,530,414 |
LanguageTechnology | OpenAI Introduces DALL-E 2: A New AI System That Can Create And Edit Realistic Images And Art From A Description In Natural Language | New research by the OpenAI team has released a new version of DALL-E, its text-to-image generation tool. [DALL-E 2](https://openai.com/dall-e-2/) is a higher-resolution and lower-latency variant of the original system, generating images based on user-written descriptions. It also has additional features, such as altering an existing image.
In January of 2021, the first DALL-E, a portmanteau of the artist “Salvador Dal” and the robot “WALL-E,” emerged, limited to AI’s capacity to visualize concepts. The researchers aimed to address the difficulties with technical safeguards and a new content policy, lower its computational load and advance the model’s basic capabilities.
Inpainting, one of the new DALL-E 2 features, applies DALL-E’s text-to-image capabilities at a finer level. Users can begin by selecting a section of an existing photograph and instructing the model to alter it. For example, users can cover a painting on a living room wall with a new picture or put a vase of flowers on a coffee table. The model can also fill (or remove) objects while considering factors such as shadow directions in a room. Variations is another function that works as an image search tool for photographs that don’t exist. Users can start with a single image and then make various modifications based on it.
[Continue Reading](https://www.marktechpost.com/2022/04/09/openai-introduces-dall-e-2-a-new-ai-system-that-can-create-and-edit-realistic-images-and-art-from-a-description-in-natural-language/)
Paper: https://cdn.openai.com/papers/dall-e-2.pdf
​
https://reddit.com/link/tzunwv/video/rywbl253nis81/player | 0.94 | t3_tzunwv | 1,649,515,566 |
LanguageTechnology | looking for a generative models company training in Germany | Hi everyone. I'm looking for a company or organization that could provide a few days long training on basic nlp in python and training generative text models with huggingface. Onsite in office in Germany. Any recommendations? | 0.88 | t3_tz5eim | 1,649,429,840 |
LanguageTechnology | Dense Passage Retriever(DPR) Open-QA System | Hi, I made a video explaining **Dense Passage Retriever(DPR)** paper. We specifically explain the End to End QA system suggested in the latter part of the paper which discusses how to build an Open-QA system using dense retrievers.
DPR was one of the first papers that discussed building dense retrievers using QA pairs only and didn't require a big pretraining computational setup like ORQA or REALM. It is currently used in a lot of places as a dense retriever. You can find [Hugginface](https://huggingface.co/docs/transformers/model_doc/dpr) and [Haystack](https://github.com/deepset-ai/haystack/blob/master/tutorials/Tutorial9_DPR_training.py) implementations also.
This video is part of a series on Open-QA using dense retrievers. We have made 2 videos on DPR. In the latter, we discuss how to build a dense retriever from scratch. Thanks for the support and it would be great if you could give any feedback.
[https://www.youtube.com/watch?v=rvcyyJNjPU0](https://www.youtube.com/watch?v=rvcyyJNjPU0) | 1 | t3_tz2z5u | 1,649,422,723 |
LanguageTechnology | Evaluating gpt2 | Hi everyone,
I'm a beginner and I'm using [https://github.com/shawwn/gpt-2](https://github.com/shawwn/gpt-2) to fine tune gpt-2. I want to know how to measure the perplexity of my fine tuned model. Is there any tutorial that can help me implementing this task ? that would save my life . | 1 | t3_tyzqw2 | 1,649,410,600 |
LanguageTechnology | Dependency parsing for Inter-chunk dependency annotated treebank | I have a treebank in Hindi which has sentences divided into chunks (NP,PP, VGF etc.) and the dependency relation of this chunk with one other chunk in the sentence, through their respective chunkheads. How can I train a dependency parser model to learn and predict these inter-chunk dependency relations.
My main issue is which format should i convert this data(SSF format) into? to train a dependency parser. Most Dparsers use the CONLL format but how can i use that for an inter-chunk learning task.
A sentence of the data format i have from the treebank.
​
1 (( NP <fs name='NP1' drel='k1:VGF'>
1.1 rAma NNP
))
2 (( NP <fs name='NP2' drel='k2:VGF'>
2.1 PZala NN
))
3 (( VGF <fs name='VGF'>
3.1 KAwA VM
3.2 hE VAUX
))
The above SSF shows the relations between chunk heads. The fourth field of the SSF contains the dependencies. The head is identified by a unique id using an atrribute value pair. This can be seen above at node no. 3, where ‘name’ is given an id ‘VGF’. The dependents are then related to the head using ‘drel’ attribute. The value for drels is “dependency relation:head id” . The fourth field also has other morpholigical data for the word but i have edited those out here. | 1 | t3_tyxhza | 1,649,400,563 |
LanguageTechnology | Tensorflow-Transformers 2.0 ( for NLP, CV, Audio ) | Hi Everyone,
I am delighted to announce, I am releasing Tensorflow-Transformers version 2.0 soon. It comes with lot of speedup and designed specifically for Tensorflow users, who are interested in using SOTA Transformer models in NLP, CV, AUDIO etc.
Text-Generation is 90 times faster comparing to Huggingface TF implementation and other equivalent TF implementation in most cases. Designed with complete serialisation in mind, it offers a lot of other features.
We have custom tokenizers using tf-text, end to end serialization pipeline and many more.It supports GPU, TPU, mixed precision, TFlite by default.
Currently supporting 12 major models Bert, Albert, Roberta, CLIP, VIT, T5, GPT2, mT5, BART and SentenceTransformers etc.
Have a look and share your feedback, open issues . Thanks.
Code : [GitHub - legacyai/tf-transformers: State of the art faster Natural Language Processing in Tensorflow 2.0 . 1](https://github.com/legacyai/tf-transformers)Website : [https://legacyai.github.io/tf-transformers 1](https://legacyai.github.io/tf-transformers)
&#x200B;
\- \[Read and Write TFRecords using tft\](https://github.com/legacyai/tf-transformers/blob/main/tutorials/1\_read\_write\_tfrecords.ipynb)
\- \[Text Classification using Albert\](https://github.com/legacyai/tf-transformers/blob/main/tutorials/2\_text\_classification\_imdb\_albert.ipynb)
\- \[Dynamic MLM (on the fly pre-processing using tf-text) in TPU\](https://github.com/legacyai/tf-transformers/blob/main/tutorials/3\_masked\_lm\_tpu.ipynb)
\- \[Image Classification ViT multi GPU mirrored\](https://github.com/legacyai/tf-transformers/blob/main/tutorials/4\_image\_classification\_vit\_multi\_gpu.ipynb)
\- \[Sentence Embedding train from scratch using Quoara on Roberta + Zeroshot STS-B\](https://github.com/legacyai/tf-transformers/blob/main/tutorials/5\_sentence\_embedding\_roberta\_quora\_zeroshot.ipynb)
\- \[Prompt Engineering using CLIP\](https://github.com/legacyai/tf-transformers/blob/main/tutorials/6\_prompt\_engineering\_clip.ipynb)
\- \[Question Answering as Generation - Squad v1 using GPT2\](https://github.com/legacyai/tf-transformers/blob/main/tutorials/7\_gpt2\_question\_answering\_squad.ipynb)
\- \[Code to Code translation (CodexGLUE - Java to C#) using T5\](https://github.com/legacyai/tf-transformers/blob/main/tutorials/8\_code\_code\_java\_to\_csharp\_t5.ipynb)
| 0.95 | t3_tyvdst | 1,649,392,239 |
LanguageTechnology | Google AI Introduces a Common Voice-Based Speech-to-Speech Translation Corpus (CVSS) That Can Be Directly Used For Training Direct S2ST Models Without Any Extra Processing | Speech-to-speech translation is the automatic translation of speech from one language to speech in another (S2ST). S2ST models have been widely accepted for bridging communication gaps between persons who speak different languages.
S2ST systems are traditionally developed with a text-centric cascade comprising automatic speech recognition (ASR), text-to-text machine translation (MT), and text-to-speech (TTS) synthesis subsystems. Recent studies have introduced S2ST, which does not rely on intermediary text representation. However, there are currently very few publicly available corpora directly relevant for such research.
A new Google study has released [CVSS](https://arxiv.org/pdf/2201.03713.pdf), a Common Voice-based Speech-to-Speech translation corpus. From Arabic to Slovenian, CVSS provides sentence-level parallel speech-to-speech translation pairs into English from 21 languages. The Common Voice project used 1153 hours of crowdsourced human volunteer recordings to create the source talks in these 21 languages.
[Continue Reading](https://www.marktechpost.com/2022/04/07/google-ai-introduces-a-common-voice-based-speech-to-speech-translation-corpus-cvss-that-can-be-directly-used-for-training-direct-s2st-models-without-any-extra-processing/)
Paper: https://arxiv.org/pdf/2201.03713.pdf
Github: https://github.com/google-research-datasets/cvss | 1 | t3_tyg7n5 | 1,649,347,182 |
LanguageTechnology | Gaining Psychological insights from text analysis | I'm trying to look for some research papers that used text analysis (mostly from social media) to gain social psychological insights, similiarly to what Cambridge Analytica did.
In particular i would like to understand the algorithms used to mine this sort of data from the text and what psychological insights can be gained.
For example, i would like to measure the coefficient of polarization for a certain group, how would i perform this ? What algorithms should i look for ? Can i do this with just python or should i use something else ?
I have some knowledge with python and i would like to try perform this type of research but i'm not finding much informations about what to actually look for inside the text and how to analyze it. | 0.5 | t3_tydka7 | 1,649,339,500 |
LanguageTechnology | How to identify covariate shift with text data? | Let say, I am building a model (NER, or let say classification into sports categories) on a stream of data (short summaries) which are all related to sports. Now let say this stream data has been changed over the period and now it's a mixture of sports and finance.
So how do we identify whether distribution of our new data has been changed? Or in a very naive terms, when do we decide to retrain our model?
Let say our model is being deployed over the production environment, so we can not wait until someone report us that model is not performing well. I am exploring some active approach to solve this issue. Maybe like periodic checking, or everyday checking.. but still how to check, what to check??
I consider these two ways to do this. But not sure, whether these are correct or not.
1. Using word distribution of training corpus and new data. After that perform chi-square test.
2. Using length wise distribution of training corpus and new data. After that chi-square test.
Let me know, if someone has worked in this field before and help me understand this. | 1 | t3_ty7qho | 1,649,316,888 |
LanguageTechnology | 👉 Meet GPT-NeoX-20B, A 20-Billion Parameter Natural Language Processing AI Model Open-Sourced by EleutherAI | In the latest AI research breakthrough, researchers from EleutherAI open-sourced GPT-NeoX-20B, a 20-billion parameter natural language processing AI model similar to GPT-3. The model was trained on nearly 825GB of publicly available text data and performed comparably to GPT-3 models of similar size. It’s the world’s largest dense autoregressive model with publicly accessible weights. GPT-NeoX-20B obtained an accuracy similar to a linear interpolation between OpenAI’s Curie and DaVinci models when tested on various typical NLP benchmark tasks and its one-shot performance on the MATH test dataset outperformed GPT-3 175B. GPT-NeoX-20B, according to EleutherAI, is the world’s largest open-source pre-trained autoregressive language model.
OpenAI announced the GPT-3 model with 175B parameters in 2020 but did not provide the trained model files. Instead, OpenAI offered an API that allows developers to use web service calls to integrate the model into their programs. Megatron-11B, Pangu-13B, Meta’s Fairseq 13B, and EleutherAI’s early models, GPT-Neo and GPT-J-6b are among the larger models that have been open-sourced since then.
[Continue Reading](https://www.marktechpost.com/2022/04/06/meet-gpt-neox-20b-a-20-billion-parameter-natural-language-processing-ai-model-open-sourced-by-eleutherai/)
Paper: http://eaidata.bmk.sh/data/GPT\_NeoX\_20B.pdf
Github: https://github.com/EleutherAI/gpt-neox | 0.95 | t3_ty4j65 | 1,649,304,328 |
LanguageTechnology | IBM Watson NLU API inconsistencies | I'm testing the Watson NLU engine and it sure looks like it's not up to the task, even on the following simple case. Anyone have suggestions on an engine that can provide a better sentiment analysis?
**Test 1** (gives a sentiment value I expect)
"text": "WidgetX has been recently created, and it is terrible."
Output:
"keywords": [
{
"text": "WidgetX",
"sentiment": {
"score": -0.964476,
"label": "negative"
},
"relevance": 0.5,
"count": 1
}
],
"entities": [
{
"type": "Organization",
"text": "WidgetX",
"sentiment": {
"score": -0.964476,
"label": "negative"
},
**Test 2** (gives a very wrong sentiment)
"text": "WidgetX has been recently created. It is terrible."
Output:
"keywords": [
{
"text": "WidgetX",
"sentiment": {
"score": 0,
"label": "neutral"
},
"relevance": 0.5,
"count": 1
}
],
"entities": [
{
"type": "Organization",
"text": "WidgetX",
"sentiment": {
"score": 0,
"label": "neutral"
}, | 1 | t3_ty43sa | 1,649,302,891 |
LanguageTechnology | Amazon Researchers Developed a Universal Model Integration Framework That Allows To Customize Production Voice Models in a Quick and Scalable Way | Alexa and other voice assistants frequently use a range of speech synthesizers, which varies in terms of expressivity, personality, language, and speaking style. The machine learning models that underpin these applications can have vastly diverse architectures. Integrating them into a single voice service is time-consuming and difficult.
A new Amazon research presents a universal model integration framework that enables quick, scalable customizable production voice models.
[Continue Reading](https://www.marktechpost.com/2022/04/06/amazon-researchers-developed-a-universal-model-integration-framework-that-allows-to-customize-production-voice-models-in-a-quick-and-scalable-way/) | 1 | t3_txv2qi | 1,649,275,957 |
LanguageTechnology | Who, What & How | Given a sentence, what's the state of the art method to extract the "who" "what" and "how"?
Whether the technique is extractive or abstractive, based on transformers or POS tagging, please drop few words below! | 0.5 | t3_txt0h0 | 1,649,270,370 |
LanguageTechnology | Auto generated text based on many conditions | Hi,
I am new to language processing. I have to do some research on a specific use cases:
The input values are small product descriptions e.g. and a category e.g. "beverage":
* COCA COLA LIGHT 20x0,33L GLAS
Output:
* Coca Cola Light 0,33L
Based on the product category there are many conditions as how the output has to look like (e.g. the input contains the word "vegan" it has to be the first word in the output etc.). What are some keywords for me to start researching? I came across models like " GPT-3", but from what I understood these are more for natural long text creations and not for outputs based on many conditions?
&#x200B;
So what I am looking for is to get some keywords to guide me in the right direction
thanks guys | 1 | t3_txs7ul | 1,649,268,196 |
LanguageTechnology | Methods for measuring inter-word similarity or relatedness | What are common (traditional) methods to estimate the degree of semantic similarity or relatedness between words?
I am looking for a reasonable baseline to compare "cosine similarity on word embeddings" with. | 1 | t3_txpuw3 | 1,649,261,793 |
LanguageTechnology | Stanford Researchers Propose ‘Time Control (TC)’: A Language Model that Uses Stochastic Processes to Enhance the Efficiency and Coherence of Long Text Generation | Writing a few lines is an easy chore for most individuals, but even seasoned authors frequently run into difficulties when trying to construct their second chapter. A similar problem plagues today’s large-scaled pretrained language models, such as GPT-2, which excel at short text production but degrade into incoherence when used for lengthier texts. The incapacity of such models to plan or reflect long-range dynamics might be blamed for the failure to evolve texts from beginning to conclusion correctly.
To address these challenges, a Stanford University research team introduced Time Control (TC), a language model that implicitly plans using a latent stochastic process and seeks to generate sentences that follow this secret plan. Human assessors scored the outputs 28.6 percent higher than baseline approaches, indicating that the unique strategy enhances performance on long text production.
[Continue Reading](https://www.marktechpost.com/2022/04/06/stanford-researchers-propose-time-control-tc-a-language-model-that-uses-stochastic-processes-to-enhance-the-efficiency-and-coherence-of-long-text-generation/)
Paper: https://arxiv.org/pdf/2203.11370.pdf
Github: https://github.com/rosewang2008/language\_modeling\_via\_stochastic\_processes | 1 | t3_txnuow | 1,649,256,470 |
LanguageTechnology | Word representations with positive weights | The dense vectors usually contain negative and positive values for features. Is there a representation scheme apart from TFIDF and CountVectoriser to positively weight the features for text classification? | 0.67 | t3_txmygu | 1,649,253,920 |
LanguageTechnology | An intuitive explanation to Singular Value Decomposition. #MathsForMachineLearning | nan | 0.29 | t3_txil0z | 1,649,239,041 |
LanguageTechnology | Researchers From Allen Institute for AI Introduce ‘MERLOT Reserve’: A Novel Multimodal Video Question Answering Model | We humans navigate the environment using all of our senses. Allen Institute researchers propose MERLOT Reserve, a model that learns to represent videos over time and across several modalities, including audio, subtitles, and video frames. It was trained using a new learning objective and more than 20 million YouTube videos.
MERLOT Reserve is a unique, cutting-edge methodology for solving video-related inquiries. MERLOT Reserve can dependably choose the correct answer from a selection of multiple-choice answers when given a video and a question. This forecast is made by MERLOT Reserve jointly reasoning over the visual frames of the video, the video subtitles, and the audio in the movie.
[Continue reading this cool research update from AI2](https://www.marktechpost.com/2022/04/05/researchers-from-allen-institute-for-ai-introduce-merlot-reserve-a-novel-multimodal-video-question-answering-model/)
Paper: https://arxiv.org/pdf/2201.02639.pdf
Demo: https://merlot-reserve.apps.allenai.org/
Project: https://rowanzellers.com/merlotreserve/
Github: https://github.com/rowanz/merlot\_reserve | 1 | t3_tx2g8n | 1,649,185,685 |
LanguageTechnology | Classifying sections of a document | Hi! I work with clinical documents and am trying to develop a method of section classification. Rules-based methods perform okay, but I'm curious about deep learning methods as well. The only neural approach I've seen is [http://www.oeft.de/su/pdf/specom2018.pdf](http://www.oeft.de/su/pdf/specom2018.pdf), and it only does binary classification of boundaries.
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I was thinking of using a longformer and using token classification modeled after NER. But these would be really long "entities". Anybody have any experience/recommendations for this type of problem? | 1 | t3_twx1ik | 1,649,171,083 |
LanguageTechnology | Query Generation | I'm trying to create a search system that pulls in relevant documents from a database that may be needed to continue writing the next portion of what someone is writing.
i.e. the writing so far: "I've been living here for 15 years. My home address"
In this case, the query should return a document containing my home address to fill this in.
How could this be done? | 1 | t3_twopvs | 1,649,141,134 |
LanguageTechnology | Google AI’s Latest 540-Billion Parameter Model (Pathways Language Model Called PaLM) Unlocks New Tasks Proportional To Scale | In recent years, large neural networks trained for language recognition and creation have shown remarkable outcomes in various tasks. GPT-3 demonstrated that large language models (LLMs) could be utilized for few-shot learning and achieve outstanding results without significant task-specific data or model parameter modification. Recent LLMs, including GLaM, LaMDA, Gopher, and Megatron-Turing NLG, have scaled model size, used sparsely activated modules, and trained on larger datasets from more diverse sources to attain state-of-the-art few-shot performance on numerous tasks.
In a recent [research paper](https://storage.googleapis.com/pathways-language-model/PaLM-paper.pdf), Google researchers introduced [Pathways Language Model (PaLM)](https://storage.googleapis.com/pathways-language-model/PaLM-paper.pdf). PaLM is a 540-billion parameter, dense decoder-only Transformer model learned with the Pathways system that allowed efficient training of a single model across several TPU v4 Pods. PaLM was tested on hundreds of language understanding and generation tasks, and it was discovered that it achieved state-of-the-art few-shot performance across the board, in many cases by a large margin.
Read this summary in a little more [detail Here](https://www.marktechpost.com/2022/04/04/google-ais-latest-540-billion-parameter-model-pathways-language-model-called-palm-unlocks-new-tasks-proportional-to-scale/)
Paper: [https://storage.googleapis.com/pathways-language-model/PaLM-paper.pdf](https://storage.googleapis.com/pathways-language-model/PaLM-paper.pdf)
Google blog: https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html | 1 | t3_two1h3 | 1,649,138,304 |
LanguageTechnology | Microsoft Researchers Introduce ‘Jigsaw’: An AI Tool To Augment Large Language Models (GPT-3, Codex, etc.) By Deploying Post-Processing Techniques That Understand The Programs’ Syntax And Semantics | GPT-3, Codex, and other sizable pre-trained language models can be adjusted to create code from natural language descriptions of programmer intent. Every developer in the world might benefit from these automated models, which have the potential to increase productivity. However, because the models may fail to understand program semantics, the quality of the generated code cannot be guaranteed.
Microsoft researchers introduce Jigsaw, a new tool that can help these big language models perform better. Jigsaw is a Python Pandas API code generator that accepts multi-modal inputs. Jigsaw uses post-processing techniques to decipher the syntax and semantics of programs and then uses user feedback to improve future performance.
[**Continue Reading**](https://www.marktechpost.com/2022/04/04/microsoft-researchers-introduce-jigsaw-an-ai-tool-to-augment-large-language-models-gpt-3-codex-etc-by-deploying-post-processing-techniques-that-understand-the-programs-syntax-and-se/)
Paper: https://arxiv.org/pdf/2112.02969.pdf
Dataset: https://github.com/microsoft/JigsawDataset | 0.96 | t3_tw9158 | 1,649,096,448 |
LanguageTechnology | Scalable framework lets multiple text-to-speech models coexist | nan | 1 | t3_tw8nd6 | 1,649,095,504 |
LanguageTechnology | Run Sparse ONNX models on the DeepSparse Engine | Hey all, want to share a new revamped README for the open-source DeepSparse Engine repo.
The code base allows anyone to run sparse ONNX models on top of popular frameworks (e.g. Transformers) for ultra fast speed-ups in inference performance. A few of the highlights include the DeepSparse Server (built on top of FastAPI) to deploy your models and the DeepSparse Benchmark to see how fast your models run.
Let me know what you think and if want to see anything else appear on the repo! 🦾
[https://github.com/neuralmagic/deepsparse](https://github.com/neuralmagic/deepsparse)
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LanguageTechnology | Edinburgh NLP taught masters vs cambridge research masters | I was wondering if anyone here has done the Edinburgh masters and has any thoughts on it vs a research masters. Would the taught course be more likely to give me stronger practical skills compared to a more self-directed research programme focusing on a narrower part of NLP? I'm coming from a linguistics background + mostly self-taught on the computational side of things, so going through a more rigorous, organised course is kind of appealing. I think the end goal is going into industry after the masters, not entirely sure if I might want to end up doing a phd - if that affects where I should go. thanks in advance :) | 1 | t3_tw5n84 | 1,649,088,185 |
LanguageTechnology | Natural Language Query (NLQ) in BI: A brief history and comparison | nan | 0.25 | t3_tw2eb6 | 1,649,079,941 |
LanguageTechnology | Questions regarding Empath module | Hi,
I'm using Empath module ([link](https://www.researchgate.net/publication/301872654_Empath_Understanding_Topic_Signals_in_Large-Scale_Text)) to analyze topics of texts. Is there any way I can check which specific word/ term is categorized into which general big topic (e.g 'violence', 'negative\_emotion',...) in the Module?
Thanks for your help! | 0.81 | t3_tvymcd | 1,649,067,741 |
LanguageTechnology | Preparing a dataset for seq2seq transforners | Hello there,
I am sorry for the stupid question. Are there any tutorials on making datasets for BART fine-tuning? I mean, suppose I have a parallel Hungarian-Polish corpus. I split it into one simple text file in Polish,and the second in Hungarian. How does one transform these into a dataset that is suitable for fine-tuning pre-trained BART model? Are there any notebooks, probably? | 1 | t3_tvlbov | 1,649,023,433 |
LanguageTechnology | Determine text quality | I very often work with unlabeled and unstructured reviews data. You wouldn’t believe what “adults” will write when they assume the text wouldn’t ever see the light of day.
Anyways, I want to calculate a quality metric in order to identify quality and poor text reviews. My first thought went to perplexity and coherence but if someone has any better ideas feel free to share. | 1 | t3_tvtihw | 1,649,047,503 |
LanguageTechnology | What are the unsupervised/deep learning methods used for aspect extraction without the use of any seed or candidate words? | There are several methods for AE, but each uses some seeds words which are already pre-defined. Are seed words always necessary for unsupervised methods? What are the properties an aspect has in a sentence for aspect extraction? What are the problems or challenges with the current research in the area of aspect extraction (AE)?
I tried the spacy and text blob library, which are useful for only short texts and have limitations. The research papers I read so far used annotated dataset for the experiments and uses a pre-defined list of aspects and find their synonyms or similar words to other aspects. So, I want to know about any experiments done without the use of these candidate words.
I am trying to start my research in the aspect term extraction (ATE) area. Any resources that can be used as a guideline will be very helpful.
[https://stackoverflow.com/questions/71640984/what-are-the-unsupervised-deep-learning-methods-used-for-aspect-extraction-witho](https://stackoverflow.com/questions/71640984/what-are-the-unsupervised-deep-learning-methods-used-for-aspect-extraction-witho) | 1 | t3_tvo3fd | 1,649,031,089 |
LanguageTechnology | Twitter Releases ‘Qurious’ For Next-Generation Data Insights Using Natural Language Queries | Twitter processes over 400 billion events in real-time and generates data on a petabyte (PB) scale. One of the most significant challenges with current data-consumption systems is the requirement for backroom processing. Before consumption, engineers and analysts must build dashboards, reports, and other items. This creates a lower data time value, affecting Twitter’s ability to make timely data-driven decisions.
The entire cost of obtaining insights from additional traits, features, and dashboards has increased. Current technologies don’t foresee and proactively uncover insights from exabytes of data based on what our internal business customers could find beneficial, resulting in missed opportunities.
Many studies suggest a comprehensive and resilient big data platform’s infrastructure for data processing, storage, and data consumption. We have robust infrastructure across the industry for processing petabytes of data and storing large amounts of data, such as distributed blob stores. However, obtaining timely, meaningful, and actionable insights from these exabyte-scale data systems via dashboards, visualizations, and reports remains non-trivial.
Advances in natural language processing and machine learning have made it possible to make data consumption from exascale platforms for insights both easy and timely.
Twitter has recently released Qurious, a new in-house product that allows internal business customers to ask inquiries in natural language. The product consists of a web app and a Slack chatbot connected to BigQuery and Data QnA APIs. The Slack chatbot was created with node.js and the Express Framework, based on a Google Data QnA reference implementation. They are then offered real-time analytics without having to construct dashboards.
[**Continue Reading**](https://www.marktechpost.com/2022/04/03/twitter-releases-qurious-for-next-generation-data-insights-using-natural-language-queries/) | 0.95 | t3_tvlu44 | 1,649,024,757 |
LanguageTechnology | Glossary Maker | I'm looking for a tool that can analyze an old book that uses obscure words — say, Burton's Arabian Nights — pick out all the obscure words, and make a list of them. A tool that could be used to help create a glossary for a given text. It doesn't need to define the words: only find them. I found a thread here from seven years ago where someone was asking for something like that, but it didn't really help. It included various lists, but no good tool for actually using them. | 1 | t3_tvkg4j | 1,649,021,205 |
LanguageTechnology | Help me choose between NLP graduate programs | I have a choice between two universities for post graduate study.
A: US uni, rank around 20 globally. 2 year program. I will be like any regular student and will be very broke/in debt.
B: Rank around 100, not in the US (I speak the language of its country). Generous funding with no work/teaching obligations, and the department seems prepared to support me a lot with research. They want me to stay 5 years for a PhD.
I am a US citizen and ultimately hope to have the option to work for US companies one day due to the high salary.
I was going to go with B due to the extensive support, but a few people I know are insisting I'd be destroying my future prospects for mere transient comfort. Arguments include "rank matters," "US companies won't value a non-US degree," and "PhDs are not worth it."
Should I switch to A and suffer through the two rough years? | 0.5 | t3_tv510x | 1,648,976,366 |
LanguageTechnology | can NLP professor teach CV? | I'm 1st year in phd and my interest lies in medicine/bio so i think probably CV fits me
but prof who's specializing in CV doesn't take me
while prof whose specialty is NLP is willing to take me.
Can I go for the latter prof?
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p.s if my subject is Clustering algorithm, can NLP prof teach me? | 0.43 | t3_tv06bu | 1,648,957,626 |
LanguageTechnology | NLP for music generation | Hi everyone! I have been trying to use NLP to generate music. My site is currently using GPT-3's API which is very slow and I would like to train my own model to speed up requests and remove a dependency on someone else's service. I have trained GPT-NEO from scratch for 1M steps on a custom dataset but the results have been... not very good. I am wondering what Transformer models/methods would be best. I am using this project as reference: [https://github.com/chrisdonahue/LakhNES](https://github.com/chrisdonahue/LakhNES)
I have converted a subset of the LAKHMIDI dataset into text (about 17k songs) and am thinking of using that for training from scratch, before fine-tuning on my NES dataset (3k songs). Size is also an issue I have run into. A lot of these models only support 2048 tokens, and songs can be much longer. I'm not sure how to get around this other than using something like GPT-XL. I have used HuggingFace very sparingly and am very new to NLP.
If you want to see the current song quality, my website is [here](https://www.chiptune.app), keep in mind generation may take a while. Thanks! | 1 | t3_tuoc5v | 1,648,920,664 |
LanguageTechnology | Real-Time Speech-to-Text in less than 20 lines of Python Code | nan | 1 | t3_tu4002 | 1,648,853,723 |
LanguageTechnology | The Token-Dropping Approach Used By ML Researchers From Google and NYU Reduces BERT Pretraining Time And Cost By 25% | The Pretraining of BERT-type large language models, which may scale up to billions of parameters, is essential to achieving best-in-class performance on various natural language processing (NLP) applications. However, the pretraining procedure is costly, and it has become a hurdle for the industrial deployment of big language models.
In a research paper, researchers from Google, New York University, and the University of Maryland recommend a simple but effective “token dropping” method that drastically reduces the pretraining cost of transformer models like BERT while maintaining downstream fine-tuning performance.
Token dropping is a technique for speeding up the pretraining of transformer models like BERT without sacrificing their performance on downstream tasks. Starting with an intermediate layer in the model, they eliminate uninteresting tokens to let the model focus on key tokens more effectively, given its limited computing resources. The model’s last layer then picks up the dropped tokens, producing full-length sequences. They use the built-in masked language modeling (MLM) loss and its dynamics to detect non-essential tokens with little computing complexity. According to their tests, this straightforward strategy decreases BERT’s pretraining cost by 25% while yielding somewhat higher overall fine-tuning performance on conventional downstream tasks.
[Continue Reading The Summary](https://www.marktechpost.com/2022/04/01/the-token-dropping-approach-used-by-ml-researchers-from-google-and-nyu-reduces-bert-pretraining-time-and-cost-by-25/)
Paper: https://arxiv.org/pdf/2203.13240.pdf
Github: https://github.com/tensorflow/models/tree/master/official/projects/token\_dropping | 1 | t3_tu2gn4 | 1,648,849,573 |
LanguageTechnology | Topic classification in a dialogue corpora | I have many transcripts of interviews which are meant to be represented in an annotated linguistic corpora. Among other parameters as pos-tagging and for e.g. speech disfluencies annotation I need to tag topics inside each dialogue. I have a list from about 12 topics and I want to use such an algorithm which would detect and classify topics according to my list.
Part of the corpora is annotated. I tried TF-IDF to extract key-words for each topic. It worked but I still have no strategy for what do I need to do next. Seems like I have to deal with multi class or multi label classification.
Would much appreciate any advices! | 0.84 | t3_ttw5x3 | 1,648,833,533 |
LanguageTechnology | Search system at LinkedIn (Research Paper Walkthrough) | This paper from LinkedIn discusses exactly that and tries to answer few interesting questions regarding deploying deep NLP systems in the production environment
Interested to know more? then watch video summary at - https://lnkd.in/dap-vQ4N
⏩ Paper Title: Deep Natural Language Processing for LinkedIn Search Systems
⏩ Paper: https://arxiv.org/abs/2108.08252
⏩ Author: Weiwei Guo, Xiaowei Liu, Sida Wang, Michaeel Kazi, Zhoutong Fu, Huiji Gao, Jun Jia, Liang Zhang, Bo Long
⏩ Organisation: LinkedIn | 1 | t3_tts1c2 | 1,648,823,330 |
LanguageTechnology | Pattern Matching using Entities | I know you can search for patterns in text using Matcher and pos tags in spaCy. But is it possible to search for patterns using entities?
I want to be able to extract phrases such as "Mary (1990)", "Mary and Lily (2000)", "University of Reddit (2022)". So, the patterns should be something like (PERSON, DATE), (ORG, DATE).
Would appreciate some help or direction on how to go about doing this. | 0.72 | t3_ttkt4m | 1,648,797,307 |
LanguageTechnology | Pro simplification software | I’m an NLP hobbiest and noob. I’m looking for a program that will simplify text to a particular grade/Lexie level. I found some libraries based off of the Newsela corpus, but it is probably beyond me to get it all running. I’ve found a few websites as well but they are not great. I’m creating social studies content and my writers are bad at leveling. | 0.67 | t3_ttc760 | 1,648,767,733 |
LanguageTechnology | Training BERT models on distinct text fields, best approach? | I am using BERT models for text classification. The application is literature classification, where I am utilizing both the title and abstracts of the papers for classification.
I am wondering what is the best way to classify things that have distinct text fields. Currently, I am just using string concatenation to create a long string containing the two fields *e.g.* for a paper with the title: "whatever the title is" and abstract:"this is the abstract" it becomes "whatever the title is this is the abstract". But this loses the context of these fields being distinct.
I have considered prepending the fields with a label *e.g.* "TITLE: whatever the title is ABSTRACT: this is the abstract". I feel this may provide more context to the model. But I am wondering if there is a more standard and better way to do this. I am sure a similar problem has been solved for instance with email subject vs. text. Do I just need to train separately on the different fields? | 1 | t3_tt4zha | 1,648,747,822 |
LanguageTechnology | How to find schools offering a masters in computational linguistics? | Is there a website or something with a master list of schools who offer a masters degree related to computational linguistics? I am just trying to broaden my horizons beyond the obvious top 10 schools before I start applying for grad school here in the next few months. It could be in Europe or North America, not very picky about location. Thank you in advance. | 0.8 | t3_tt4j6d | 1,648,746,605 |
LanguageTechnology | DeepL API | Tried using this command from the DeepL API and am getting nothing in response, no error message either.
https://www.deepl.com/en/docs-api/
curl https://api-free.deepl.com/v2/translate \
-d auth_key=[yourAuthKey] \
-d "text=Hello, world!" \
-d "target_lang=DE"
I used my auth key but maybe there’s something wrong with it.
Might anyone who has had success with it know what the issue is?
Thank you | 0.33 | t3_tt0qwq | 1,648,736,218 |
LanguageTechnology | Debug, inspect and run semantic search applications with the txtai console | nan | 1 | t3_tsyzy8 | 1,648,730,890 |
LanguageTechnology | Data set: Central Bank Speeches Corpus 1997-2022 | Hi, I recently wrote a [paper](https://arxiv.org/abs/2109.10058) where I scraped speeches from central banks. The data set contains speeches held by central bank boards affiliated with the Bank for International Settlements (BIS) (118 institutions) over the period 1997-01-07 to 2022-03-02. I thought I'd share it here if anyone is interested!
- [Github: Code for scraping](https://github.com/HanssonMagnus/scrape_bis)
- [Kaggle: Data set](https://www.kaggle.com/datasets/magnushansson/central-bank-speeches)
Cheers! | 1 | t3_tsc9gg | 1,648,662,958 |
LanguageTechnology | Research using GPT-3? | Hi, I'm currently working on a project to generate short text using generative models such as GPT3 or BART. I was wondering how research on GPT3 is reported as? You can't possibly just report how much accuracy your prompts are returning, it doesn't seem like much. Does anyone have any references on GPT3 (or text generation) research is done? | 1 | t3_tsc4yr | 1,648,662,629 |
LanguageTechnology | Hackernews classified by topic with Huggingface | nan | 0.82 | t3_ts9mp6 | 1,648,655,852 |
LanguageTechnology | Generative Pseudo Labeling (GPL): Unsupervised Learning in Semantic Search | Hi all, I wanted to share an article I wrote covering [Generative Pseudo-Labeling (GPL)](https://www.pinecone.io/learn/gpl/). It's a really cool technique from Kexin Wang and co (N Reimers' UKPLab) that allows us to adapt sentence transformer models to new domains using nothing more than unlabeled text data. It works by generating synthetic (query, positive, negative) triplets and margin scores, which are then used with margin MSE loss to fine-tune the sentence transformer.
Very interesting technique, if you're working in the space I'd recommend looking into it, I think there will be more work on it in the future too :) | 1 | t3_ts6c0h | 1,648,646,533 |
LanguageTechnology | BioScope corpus clinical notes, where to find | Hello,
I am trying to find a **subset of the BioScope Corpus**, concretely the **Clinical notes.** This file was hosted in a webpage [http://www.computationalmedicine.org/catalog](http://www.computationalmedicine.org/catalog)
According to BioScope [webpage](https://rgai.inf.u-szeged.hu/node/105):
***Clinical free-texts****: The radiology report corpus that was used for the* [*CMC clinical coding challenge*](http://ncc.cchmc.org/prod/pestianlabdata/request.do)*. The negation/hedge annotated version of the corpus can be obtained (due to licencing issues) by downloading the original 'ICD-9-CM coding' corpus from Cincinatti Children's Hospital site and merge it with our annotation:* [*readme*](https://rgai.sed.hu/file/44#overlay-context=)*,* [*merger software*](https://rgai.sed.hu/file/43#overlay-context=)*.*
The links are dead:
[https://ncc.cchmc.org/prod/pestianlabdata/request.do](https://ncc.cchmc.org/prod/pestianlabdata/request.do)
[http://www.computationalmedicine.org/catalog](http://www.computationalmedicine.org/catalog)
&#x200B;
Is there any available source for getting this corpora,
Thanks, | 0.81 | t3_ts33d2 | 1,648,635,005 |
LanguageTechnology | NLP & Lojban | It surprises me that there seem to be few people doing NLP with Lojban, a constructed language which is supposed to be syntactically unambiguous.
I think it's logical to assume that if someone were successful with NLP with Lojban, it'd be a big breakthrough in NLP, because it would allow computers to better "understand" language.
Thoughts on this? | 0.75 | t3_ts2fsj | 1,648,632,058 |
LanguageTechnology | Need help getting started in Speech Diarization | I want to build a speech-to-text system for audio input having 2 speaker. The goal is to differentiate words spoken by two different speakers.
I haven't worked with audio before. So want to know if there're any deep learning architectures for such task or do I have to do speech processing. | 1 | t3_ts1spw | 1,648,629,100 |
LanguageTechnology | Can I do more epochs on a saved HuggingFace model? | This can be pretty obvious, so thanks for bearing with me!
Let's say I trained a huggingface model for 3 epochs and then I saved the model. Can I reload the model and do more epochs from where I left off? | 1 | t3_tryeav | 1,648,614,980 |
LanguageTechnology | AI Podcast: from NLP to Protein-folding | nan | 0.73 | t3_trft3x | 1,648,582,976 |
LanguageTechnology | Need help for recommendation system | Problem Statement: Assign a new task to a user based on tasks previously completed by a user.
E.g. Let's assume User A has completed tasks related to "mix batter" and User B has completed tasks related to "bake cake". Then, whenever there is a task to "mix batter" or similar, then it should recommend User A as the correct person to complete that task.
Require an ML/DL approach. | 0.5 | t3_treo3s | 1,648,580,420 |
LanguageTechnology | Struggling with an Extremely Low-Resource ASR | **Background:** I am a Harvard CS+Ling student trying to develop an ASR model for an extremely low-resource language: North-Eastern Neo-Aramaic. The [dataset I'm using](https://nena.ames.cam.ac.uk/audio/185/) is \~40 minutes. Each recording is 1 - 11 minutes long and the transcriptions are in a nearly-IPA-like alphabet. The transcriptions are not phonetically aligned.
I am using CTC loss to train a [generic deep learning model](https://www.assemblyai.com/blog/end-to-end-speech-recognition-pytorch/) as a baseline and it is NaN-ing out. I think it's because the transcriptions are not exact and leave out repeated phrases and imperfections of the speaker? Regardless, I've given up and am trying to learn the model with a phonetically aligned Arabic dataset then transfer learn.
**Help:** The [Arabic dataset](http://en.arabicspeechcorpus.com/) is phonetically aligned and \~240 minutes. I want to train the deep learning model with this data but I don't know how to take advantage of the phoneme time stamps provided in TEXTGRID format. CTC and other Pytorch Audio losses don't seem to include an option to pass in this information. Any help would be so much appreciated. | 0.86 | t3_tqyv6i | 1,648,557,664 |
LanguageTechnology | New to Spacy and NLP: NUM pos tag for non Number | I'm running spacy ('es_core_news_sm') on the following spanish sentence: '¿ Seguro que queréis dejarnos tan pronto ?' and get the POS tag of NUM for the token 'queréis'.
This seems so wrong that I'm not sure what to make of it - what steps can I take to improve this tag? Is there something really basic I'm not doing but should be? | 0.86 | t3_tqxbff | 1,648,552,069 |
LanguageTechnology | Computer setup recommendations | I’m in academia (in LT obviously) and have a couple of grants that I need to spend this year. Some is earmarked for conferences, but a huge chunk is for hardware.
I currently have a 27” 4k display and a MacBook Pro from 2021 but not M1. I’ve been considering the new Mac studio and adding an ultra wide curved 34”-49” 4k display. My budget is around 7-10k USD for hardware, but I certainly don’t have to spend all of it on hardware and could use any leftover for a research assistant for a few months. I do use Google Colab quite a bit so I’m wondering if the Mac studio would be overkill. I don’t mind a Linux setup but as I use my iPad Pro a lot when creating teaching videos I’m thinking a Mac addition might be more seamless.
Mainly the setup would be used for Python, some R, machine learning applications including language model creation, but also for creating teaching videos and such. I always have a ton of windows and programs open so RAM and screen space are a must.
I’d love to hear thoughts, suggestions, recommendations, and caveats regarding setups. | 1 | t3_tqrhd2 | 1,648,527,372 |
LanguageTechnology | Need help finding the right approach | Hey everyone. I'm Lukas a MD student and I'm working on a anamnesis app, which should recognize, which symptom the doctor is asking for. For example "Do you have pain when peeing?" should be labeled as asking for dysuria. Could you point me in the right direction, on how to train a model for that?
Thanks for the help in advance! | 1 | t3_tqj21d | 1,648,500,702 |
LanguageTechnology | Spotify Employs Natural Language Search/Semantic Search For Podcast Episodes | Users don’t always input the precise words they are searching for. This requires search algorithms to compensate using fuzzy matching, normalization, and even manual aliases. While these strategies are extremely beneficial to the user, they have limitations in that they cannot capture all possible ways of expressing yourself in natural language, particularly when employing natural language sentences.
Until recently, Spotify’s search was primarily based on phrase matching. For example, if a user searches for “electric vehicles climate impact,” Elasticsearch will return search results. This returned result includes everything in its indexed metadata that contains each of the query words. However, such results do not guarantee that the relevant material for this query will be returned to the user.
[Continue Reading](https://www.marktechpost.com/2022/03/27/spotify-employs-natural-language-search-semantic-search-for-podcast-episodes/) | 0.6 | t3_tq3e2k | 1,648,450,516 |
LanguageTechnology | Is it possible to measure the intensity of emotion in text using BERT? | Hello there, I'm a newbie and still learning NLP, so correct me if I am wrong! It will be a huge help.
I'm trying to measure the intensity of anger and/or fear in e-petition data, then analyze whether the intensity of emotion affects the number of signatures.
Since I could not find a quality emotion dictionary in my language, I have to create one myself.
Initially, I was going to use word2vec and make a list of words closest to "anger" and "fear", then do the usual dictionary-based sentiment analysis (tagging the intensity of emotion of each petition based on the frequency of word appearances).
Then I found out BERT has become the SOTA nowadays, and now I am wondering whether I should use BERT in my case.
From what I have read, BERT seems to be used for classifying emotion, not measuring the intensity of emotion. But since I am not an expert in this field, I wanted to make sure and ask smarter people to be sure!
**TL; DR: Is it possible to measure the intensity of emotion in text using BERT?**
Thank you so much in advance! | 1 | t3_tpzv2y | 1,648,436,939 |
LanguageTechnology | GloVe Question | Hi all! I’m trying to use visual studio to run GloVe but it can’t run the demo shell script… What does anyone here use to run GloVe? Or, is there any resources for someone who doesn’t know much about programming to follow along with? Thanks. | 0.4 | t3_tpq4a8 | 1,648,406,299 |
LanguageTechnology | Help with finding a master's thesis topic | Hi.
I'm having a hard time finding a topic for my final thesis. I've been overwhelmed by different areas, approaches, models etc. I don't want to end up with an overdone topic, but I'm also not aiming for something revolutionary that I might struggle with working on and finishing.
I started searching for a good dataset, as well as reading research papers, to hopefully find an intersection. There's always twitter data, but I still need to find an analysis or prediction that's not been overdone, or has been done but would be interesting to see with XYZ models.
I would really appreciate any advice or help to at least narrow it down to an area with a dataset and then think about models or additional steps I could add (eg data augmentation myb?).
Thanks! | 0.91 | t3_tppznu | 1,648,405,926 |
LanguageTechnology | List of Negative words for financial data | Does somebody know any resource for a list of Negative words from a finance perspective? Even domain agnostic words would work, but the list should be holistic.
I already am using [this](https://ptrckprry.com/course/ssd/data/negative-words.txt) but it lacks words like
- miss
- discrepancy
- disappear
- delete
- duplication
I am trying to find negative phrases in a document and trying to increase Recall
Thanks! | 1 | t3_tplxog | 1,648,393,978 |
LanguageTechnology | Few-shot NER | https://youtu.be/DnQKOuG-I_0
Few-shot NER is the task of making work named entity recognition (NER) systems when a small number of in-domain labeled data is available.
In this video, I discuss in details the inner workings of "Concise Concepts" python library that facilitates the FSL NER. 🤩 | 0.96 | t3_tpijok | 1,648,382,630 |
LanguageTechnology | Microsoft Enhances its Translator, a Microsoft Azure Cognitive Service, with Z-code Mixture of Experts (MoE) models, to Boost Efficiency and Quality | Recent advancements in machine translation are breaking language barriers and bringing people worldwide together. However, human language is so flexible that MT tasks are considered to be one of the most difficult artificial intelligence projects.
Microsoft researchers have been working on enhancing existing AI methods to build multilingual, large-scale language models that can be used in multiple settings. Their current work significantly improves the quality of production translation models by adopting a more holistic, human-centric approach to learning and understanding.
They have recently released a new service named Translator, which employs Z-code Mixture of Experts models supporting the creation of AI systems that can speak, see, hear, and understand.
**Continue Reading This Article** [**Here**](https://www.marktechpost.com/2022/03/26/microsoft-enhances-its-translator-a-microsoft-azure-cognitive-service-with-z-code-mixture-of-experts-moe-models-to-boost-efficiency-and-quality/)
Paper: https://arxiv.org/pdf/2109.10465.pdf
Github: https://github.com/microsoft/DeepSpeed | 0.9 | t3_tp4uz7 | 1,648,331,003 |
LanguageTechnology | Using Reddit commentary to derive consumer insights | nan | 0.81 | t3_tp3ugx | 1,648,327,918 |
LanguageTechnology | Universities for Masters In NLP or CL | Hi, I am a junior currently pursuing my undergrad in Computer Science. I've developed an interest and fascination for machine/deep learning, specifically natural language processing. I've enjoyed doing personal projects, participating in hacks, and working on research projects, where I could utilize the technology for creating impactful projects on NLP for social good. NLP for social good is something I'm very passionate about, and as my undergraduate studies come to an end, I wish to pursue a more formal and specialized course for NLP (for social good, if possible). However, I'm unable to find as many courses with a course plan for the same.
So I wanted to ask you guys if anyone knows any good universities which offer a good research-based NLP program (for social good, if possible). I wish to pursue this subject from an academic point of view, but I'll gladly listen to all suggestions, irrespective of their research orientation. For the location, I'm willing to relocate internationally (USA, Europe, Canada, Australia, etc.) for my courses.
Looking forward to your suggestions! | 1 | t3_toj56o | 1,648,278,696 |
LanguageTechnology | Generating Human-like Deep Questions and Fact Verification using NLP Techniques | nan | 0.89 | t3_tnkqgo | 1,648,210,943 |
LanguageTechnology | Explainability for Semantic Search | nan | 1 | t3_tn72pw | 1,648,160,228 |
LanguageTechnology | Researchers From UF Health and NVIDIA Build World’s Largest Clinical Language Generator, ‘SynGatorTron’, To Develop Better AI For Rare Disease Research and Clinical Trials | A neural network that generates synthetic clinical data is in high demand, as it is a valuable resource that researchers may use to train other AI models in healthcare. Synthetic data allows developers to create simulated patient data free of sensitive protected health information and manufactured with statistical realism. Team NVIDIA and UF Health, The University of Florida’s academic health institution, have teamed up to do precisely that.
S[ynGatorTron](https://blogs.nvidia.com/blog/2022/03/22/uf-health-syngatortron-ai-synthetic-clinical-data/) is a language model developed by the team that can construct synthetic patient profiles based on the health records it has learned from. This model is ranked #1 among the language generators available in the healthcare field, with the ability to handle 5 billion parameters.
[**Continue Reading**](https://www.marktechpost.com/2022/03/24/researchers-from-uf-health-and-nvidia-build-worlds-largest-clinical-language-generator-syngatortron-to-develop-better-ai-for-rare-disease-research-and-clinical-trials%ef%bf%bc/) | 1 | t3_tmnvxx | 1,648,141,334 |
LanguageTechnology | CU Boulder vs Penn State for Natural Language processing research | Hello All, I have got admits from CU Boulder and Penn State (UP) for MS in CS. My research interests are in NLP. Could anyone tell me how the two colleges compare against each other ? My goal is to pursue Ph.D after MS (in NLP) and later work in industry in NLP-based roles.
Kindly advise on this. | 1 | t3_tmgrjs | 1,648,135,401 |
LanguageTechnology | Few-shot NER: entity extraction without annotation and training based on GPT | Hello all,
After 1 year working extensively with GPT models (GPT-3, GPT-J, and GPT-NeoX), I think I now have a good view on what these NLP models are capable of. It appears that many traditional NLP tasks can now be achieved thanks to these large language models thanks to few-shot learning (aka "prompting", or "prompt engineering").
NER is a very good candidate because, thanks to these models, it is possible to extract any type of entity without ever annotating and training a new model. Annotation has always been a challenge that has caused many entity extraction projects to simply fail, because it is a long and tedious process.
In this article, I'm showing how easy it is to perform NER thanks to GPT and few-shot learning, without any annotation process: [https://nlpcloud.io/few-shot-ner-entity-extraction-without-annotation-training-based-on-gpt.html](https://nlpcloud.io/few-shot-ner-entity-extraction-without-annotation-training-based-on-gpt.html?utm_source=reddit&utm_campaign=fe5u8885-ed8e-21eb-ba80-5242ac13d5ja)
If you also experimented with entity extraction with GPT models, I would love to hear your thoughts. Are you, like I am, impressed by the results? And do you think it means that annotation is a thing from the past?
Thanks! | 0.93 | t3_tm3z4z | 1,648,122,006 |
LanguageTechnology | Is my laptop good enough? | Hello,
I am new here in the subreddit, so, Hello!
I'll be taking NLP and Python courses in a Translation Technologies MA program.
Here are the courses if you would like to take a closer look. I will be attending to Ghent University for my 1st year, so the relevant list of courses is 1.4
([https://studiekiezer.ugent.be/master-of-arts-in-technology-for-translating-and-interpreting-en/programma/2022](https://studiekiezer.ugent.be/master-of-arts-in-technology-for-translating-and-interpreting-en/programma/2022))
I have a macbook air 2020 i3 - when I asked them about the required specs for the coursei they said it was fine.
However, I was thinking of upgrading my laptop lately and I was considering a new apple silicon macbook.
I don't know what is the python situation with the new M1 machines.
I did some research but I couldn't be sure since I am new to computational linguistics.
My friend told me, he is an AI programmer and he graduated from Kent UK, that I would be using cloud computing platforms and unix-based machines such as macbooks offer a better experience in this regard.
My girlfriend also confirmed this, she did her MA in microbiology and used cloud computing for data science.
In the end, I want a bigger screen and I feel the i3 will slow me down.
Are M1's good for this stuff or should I stick to intel.
Also, maybe I can get a second hand 15" MacBook Pro.
If that is a viable option which generation of CPUs should I stay away from? | 0.5 | t3_tlzqsk | 1,648,104,438 |
LanguageTechnology | Laptop reccomendation to do NLP project with combination of Text Mining and ML? | Hi guys, I apologize if this topic isn't allowed here. I've researched everywhere but no one has any clue. I recently find out that my uni requires student to use a laptop with 32GB RAM and 500GB memory at least. I'm studying Text Mining.
We are going to run NLP project, load and save many data using python and jupiter notebook. I have a laptop with 4GB RAM and it has many issues with importing NLTK package back then. Not sure if it's even capable to load or save data for data analysis projects.
But when I look up, laptop with 32GB RAM isn't common and they are expensive. Do you think my old laptop would be able to handle uni work and does a laptop with 16GB RAM be enough replacement instead? And what would you reccomend?
Budget range: 700-1000 euro
I found Yoga Slim 7 PRO 14-Ryzen7 to be a match but I'm not fan of Lenovo. Mac is an option but the most affordable one only has 8GB RAM and 200GB in space. | 1 | t3_tlvxoq | 1,648,090,389 |
LanguageTechnology | Open Relation Extraction is difficult | Hi everyone,
I am currently trying to figure out what kind of relation extraction task / approach would be best for the following problem:
Given a sentence, I want to extract triples (named entity 1, relation, named entity 2). The named entity extraction step is taken care of, so now I only need the relation. So the question becomes: What should the relation look like / What relations am I looking for?
"Classical" supervised Relation Extraction is mostly focused on n classes (Born\_In, Located\_In etc.) and because of that too restricted, IMO. At the same time 96 different relations (DocRed Dataset) would capture a lot of information as well.A supervised approach will also fail to find new/unseen relations. Zero Shot Adaptability could prove handy here and I have seen models performing reasonably good but the model would not be able to just generate a new relation if it encounters something unseen.
On the other side of the spectrum, we have Open Information Extraction (OpenIE) or Open Relation Extraction (OpenRE) which focuses on extracting "any" relation and these relations would probably be represented by the verbs in the sentence.I like the idea of that because all the information would be captured (high recall) but there would probably be a lot of noise. The problem here is that I want to transfer the triples into a knowledge graph and so I would need a mapping for each extracted Relation to its corresponding knowledge graph relation. I read about Canonicalization but I am not too familiar with the concept.
I am leaning towards OpenIE / OpenRE, but I am having difficulties finding relevant papers and the SOTA models. OpenIE6 and M2OIE seem to be the newest / best ones. But the BenchIE dataset reports them to be worse than ClausIE, a model that extracts VP-mediated facts from 2013 ([source](https://arxiv.org/pdf/2109.06850.pdf)) and the Github implementation is quite bad tbh.Has anyone worked with OpenIE models before? What are your experiences?
Is anyone aware of any other approaches that are worth taking a look at? I am open to any remotely connected idea or approach to the problem because I am kind of losing my mind over finding something good.
Otherwise, I will spend the next year implementing my own OpenIE/OpenRE system. (/s hopefully)
EDIT: I will probably use AllenNLP for the OpenIE task. It seems way faster than CoreNLPs Model.
&#x200B; | 0.92 | t3_tl0o1p | 1,648,054,101 |
LanguageTechnology | Check if a detected sentence is complete? | For my NLP project I build my own model to identify sentences in a PDF document. Now I would like to check if my extracted sentences are complete sentences. Is there a simple but reliable approach or library that can do this? | 1 | t3_tkzzrj | 1,648,053,431 |
LanguageTechnology | What kind of technology should I use to get pronunciation, pos on top of tokenization? | New to ML, please bear with me if I am missing something obvious. I am writing a convertor that parses Cantonese/Chinese sentence, turning them from a simple string to objects that describe their POS and pronunciation correctly.
I am using a hashmap based bi-directional maximal segment algorithm right now, it can segment correctly, but it cannot solve the ambiguity of pos and romanization, which would depends on characters before the one with ambiguity.
Example:
Example Input: 食飯枱好長
Expected output: [
{ word: "食飯枱", pos: "noun", romanization: "sik6faan6toi2" },
{ word: "好", pos: "adv", romanization: "hou2" },
{ word: "長",
// should be "adj",
because "好" is used before pos: "adj/verb",
// should be "coeng4", because "好" is used before
romanization: "zoeng2/coeng4"
}]
If I want to handle this, can HMM or CRF(if not, what other algorithm should I be using?) handle this? Because based on the example I can found online, it seems like they can only handle label of a single facet(segmentation). Can it handle segmentation, pos and romanization at the same time? What option do I have? | 1 | t3_tkv51h | 1,648,045,612 |
LanguageTechnology | Retrieval Augmented Generation (RAG) explained | In this video, we explain RAG or Retrieval Augmented Generation. We discuss **how to do Open-QA using a generative QA model**. We discuss in detail both formulations mentioned in the paper, RAG Sequence Model and RAG Token Model. We cover the mathematical formulations and how they are done in code.
The video is part 4 of 8 video series on Open Domain Question Answering. If you are interested in Open-QA or want to know more about it do check out the playlist on "Open-Domain Question Answering" on the channel.
I will really appreciate any feedback. Thanks.
[https://www.youtube.com/watch?v=G-AV-kU6qbk](https://www.youtube.com/watch?v=G-AV-kU6qbk) | 0.81 | t3_tktsxz | 1,648,041,700 |
LanguageTechnology | Reduce a set of rules | Hy guys. I am working on my final project and i got a bit stuck. I manage to generate a set of about 4000 if rules ( e.q. If a > 23 and b <3 then c). I am trying to find a way to reduce the number of rules to get more general ones. Any idea or framework to help would be greatly appreciated. Thanks in advance guys :3. | 1 | t3_tksx4p | 1,648,038,875 |
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