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Machine Learning and NLP @ML_NLP

We share news, discussions, videos, papers, and tutorials related to Machine Learning and NLP. Subscribe on Reddit!

Joined July 2014
  • Tweets 1,663
  • Following 2,882
  • Followers 54,841
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Machine Learning and NLP@ML_NLP
16 Apr 2020
Are you interested in Machine Learning and NLP? Subscribe and contribute to the Natural Language Processing community on Reddit reddit.com/r/textdatamining/ #NLProc #MachineLearning

r/textdatamining

Welcome to /r/TextDataMining! We share news, discussions, papers, tutorials, libraries, and tools related to NLP, machine learning and data analysis.

reddit.com
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Building a Faster and Accurate Search Engine on Custom Dataset with Transformers 🤗 medium.com/analytics-vidhya/… #NLProc

Build a Faster and Accurate Search Engine on Custom Dataset with Transformers🤗 | By Shivanand Roy

In this article, we will build a search engine on a huge corpus of custom dataset with Transformers

snrspeaks.medium.com
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A visual guide to regular expressions amitness.com/regex/

A Visual Guide to Regular Expression

A mental model of how various components of a regular expression work from the bottom-up.

amitness.com
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GraphGlove: embedding words in non-vector space with unsupervised graph learning arxiv.org/abs/2010.02598 #NLProc

Embedding Words in Non-Vector Space with Unsupervised Graph Learning

It has become a de-facto standard to represent words as elements of a vector space (word2vec, GloVe). While this approach is convenient, it is unnatural for language: words form a graph with a...

arxiv.org
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The return of nearest neighbor models —or memory-based learning— to NLP: strong gains on neural MT, especially for domain adaptation arxiv.org/abs/2010.00710 #NLProc
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How can we make language models be less data-hungry? arxiv.org/abs/2009.11523 #NLProc

Grounded Compositional Outputs for Adaptive Language Modeling

Language models have emerged as a central component across NLP, and a great deal of progress depends on the ability to cheaply adapt them (e.g., through finetuning) to new domains and tasks. A...

arxiv.org
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Machine Learning and NLP retweeted
Delip Rao e/σ
@deliprao
23 Sep 2020
For my next word sense disambiguation class #nlproc
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Advancing NLP with efficient projection-based model architectures ai.googleblog.com/2020/09/ad… #NLProc

Advancing NLP with Efficient Projection-Based Model Architectures

Posted by Prabhu Kaliamoorthi, Software Engineer, Google Research Deep neural networks have radically transformed natural language processing (NLP)...

research.google
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It's not just size that matters: small language models are also few-shot learners arxiv.org/abs/2009.07118 #NLProc

It's Not Just Size That Matters: Small Language Models Are...

When scaled to hundreds of billions of parameters, pretrained language models such as GPT-3 (Brown et al., 2020) achieve remarkable few-shot performance. However, enormous amounts of compute are...

arxiv.org
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A Comparison of LSTM and BERT for Small Corpus arxiv.org/abs/2009.05451 #NLProc
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Language Interpretability Tool (LIT): a visual, interactive model-understanding tool for NLP models github.com/pair-code/lit/ #NLProc

GitHub - PAIR-code/lit: The Learning Interpretability Tool: Interactively analyze ML models to...

The Learning Interpretability Tool: Interactively analyze ML models to understand their behavior in an extensible and framework agnostic interface. - PAIR-code/lit

github.com
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Paraphrase Generation as Zero-Shot Multilingual Translation arxiv.org/abs/2008.04935 #NLProc

Paraphrase Generation as Zero-Shot Multilingual Translation:...

Recent work has shown that a multilingual neural machine translation (NMT) model can be used to judge how well a sentence paraphrases another sentence in the same language (Thompson and Post,...

arxiv.org
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Word2vec Skip-gram Dimensionality Selection via Sequential Normalized Maximum Likelihood arxiv.org/abs/2008.07720 #NLProc

Word2vec Skip-gram Dimensionality Selection via Sequential...

In this paper, we propose a novel information criteria-based approach to select the dimensionality of the word2vec Skip-gram (SG). From the perspective of the probability theory, SG is considered...

arxiv.org
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Large-scale Transfer Learning for Low-resource Spoken Language Understanding arxiv.org/abs/2008.05671 #NLProc
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Zero-Shot Learning in Modern NLP joeddav.github.io/blog/2020/… #NLProc
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Sentiment Analysis using Deep Learning datasciencecentral.com/profi… #NLProc
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Big Bird: Transformers for Longer Sequences arxiv.org/pdf/2007.14062.pdf #NLProc
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Numpy Cheat Sheet towardsdatascience.com/numpy… #DataScience

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GPT-3, a Giant Step for Deep Learning and NLP anotherdatum.com/gpt-3.html #NLProc

GPT-3, a Giant Step for Deep Learning and NLP

Can intelligence emerge simply by training a big enough language model using lots of data? OpenAI tries to do so, using 175 billion parameters.

anotherdatum.com
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Trends in Integration of Vision and Language Research: A Survey of Tasks, Datasets, and Methods arxiv.org/abs/1907.09358 #NLProc

Trends in Integration of Vision and Language Research: A Survey of...

Interest in Artificial Intelligence (AI) and its applications has seen unprecedented growth in the last few years. This success can be partly attributed to the advancements made in the sub-fields...

arxiv.org
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Advances of transformer-based models for news headline generation arxiv.org/abs/2007.05044 #NLProc
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