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Thread: Last week, a list of 100 important NLP papers ( https://github.com/mhagiwara/100-nlp-papers)">https://github.com/mhagiwara... went viral. The list is okay, but it has almost *no* papers with female first authors.
NLP is rich with amazing female researchers and mentors. Here is one paper I like for each area on the list:
NLP is rich with amazing female researchers and mentors. Here is one paper I like for each area on the list:
Area: Discourse
Modeling Local Coherence: An Entity-Based Approach
Regina Barzilay and Mirella Lapata
https://people.csail.mit.edu/regina/my_papers/coherence.pdf">https://people.csail.mit.edu/regina/my...
Modeling Local Coherence: An Entity-Based Approach
Regina Barzilay and Mirella Lapata
https://people.csail.mit.edu/regina/my_papers/coherence.pdf">https://people.csail.mit.edu/regina/my...
Area: Topic Models
Topic Modeling: Beyond Bag-of-Words
Hanna M. Wallach
http://dirichlet.net/pdf/wallach06topic.pdf">https://dirichlet.net/pdf/walla...
Topic Modeling: Beyond Bag-of-Words
Hanna M. Wallach
http://dirichlet.net/pdf/wallach06topic.pdf">https://dirichlet.net/pdf/walla...
Area: Text Classification + Machine Learning
Thumbs up? Sentiment classification using machine learning techniques
Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan
https://www.aclweb.org/anthology/W02-1011/">https://www.aclweb.org/anthology...
Thumbs up? Sentiment classification using machine learning techniques
Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan
https://www.aclweb.org/anthology/W02-1011/">https://www.aclweb.org/anthology...
Area: Automatic Text Summarization
(Many good options! But I personally like the one of the list)
Get To The Point: Summarization with Pointer-Generator Networks
Abigail See et al.
https://www.aclweb.org/anthology/P17-1099/">https://www.aclweb.org/anthology...
(Many good options! But I personally like the one of the list)
Get To The Point: Summarization with Pointer-Generator Networks
Abigail See et al.
https://www.aclweb.org/anthology/P17-1099/">https://www.aclweb.org/anthology...
Area: Parsing and Syntax
CCGbank: A Corpus of CCG Derivations and Dependency Structures Extracted from the Penn Treebank
Julia Hockenmaier, Mark Steedman
https://www.aclweb.org/anthology/J07-3004/">https://www.aclweb.org/anthology...
CCGbank: A Corpus of CCG Derivations and Dependency Structures Extracted from the Penn Treebank
Julia Hockenmaier, Mark Steedman
https://www.aclweb.org/anthology/J07-3004/">https://www.aclweb.org/anthology...
Area: Segmentation and Tagging
(A million good options, but this one was super impactful)
Feature-rich part-of-speech tagging with a cyclic dependency network
Kristina Toutanova et al
(A million good options, but this one was super impactful)
Feature-rich part-of-speech tagging with a cyclic dependency network
Kristina Toutanova et al
Area: Question Answering and Machine Comprehension
(I took this to mean the modern deep-learning centric form)
A thorough examination of the CNN/Daily Mail reading comprehension task
Danqi Chen et al
https://arxiv.org/abs/1606.02858 ">https://arxiv.org/abs/1606....
(I took this to mean the modern deep-learning centric form)
A thorough examination of the CNN/Daily Mail reading comprehension task
Danqi Chen et al
https://arxiv.org/abs/1606.02858 ">https://arxiv.org/abs/1606....
Area: Sequential Labeling & Information Extraction
Empirical Methods in Information Extraction
Claire Cardie
https://aaai.org/ojs/index.php/aimagazine/article/view/1322/1223">https://aaai.org/ojs/index...
Empirical Methods in Information Extraction
Claire Cardie
https://aaai.org/ojs/index.php/aimagazine/article/view/1322/1223">https://aaai.org/ojs/index...
Area: Machine Translation
(Many to pick from, I like how this one foreshadows some unsupervised MT)
A Statistical View on Bilingual Lexicon Extraction: From Parallel Corpora to Non-Parallel Corpora
Pascale Fung https://link.springer.com/chapter/10.1007/3-540-49478-2_1">https://link.springer.com/chapter/1...
(Many to pick from, I like how this one foreshadows some unsupervised MT)
A Statistical View on Bilingual Lexicon Extraction: From Parallel Corpora to Non-Parallel Corpora
Pascale Fung https://link.springer.com/chapter/10.1007/3-540-49478-2_1">https://link.springer.com/chapter/1...
Area: Coreference Resolution
(This is a bit niche, but I really found this work helpful when I worked in this area.)
The life and death of discourse entities: Identifying singleton mentions
M Recasens, MC de Marneffe, C Potts https://www.aclweb.org/anthology/N13-1071/">https://www.aclweb.org/anthology...
(This is a bit niche, but I really found this work helpful when I worked in this area.)
The life and death of discourse entities: Identifying singleton mentions
M Recasens, MC de Marneffe, C Potts https://www.aclweb.org/anthology/N13-1071/">https://www.aclweb.org/anthology...
Area: Clustering and Word/Sentence Embeddings
(The paper from the list seems like a good choice.)
Skip-Thought Vectors
Jamie Kiros et al
https://arxiv.org/abs/1506.06726 ">https://arxiv.org/abs/1506....
(The paper from the list seems like a good choice.)
Skip-Thought Vectors
Jamie Kiros et al
https://arxiv.org/abs/1506.06726 ">https://arxiv.org/abs/1506....
Area: Generation
(Generation has a ton of papers to pick from. Here& #39;s a classic)
Discourse strategies for generating natural-language text
Kathleen R.McKeown
https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.100.8521&rep=rep1&type=pdf">https://citeseerx.ist.psu.edu/viewdoc/d...
(Generation has a ton of papers to pick from. Here& #39;s a classic)
Discourse strategies for generating natural-language text
Kathleen R.McKeown
https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.100.8521&rep=rep1&type=pdf">https://citeseerx.ist.psu.edu/viewdoc/d...
Area: Language Modeling
(I couldn& #39;t find a classic here
https://abs.twimg.com/emoji/v2/... draggable="false" alt="đ" title="EnttĂ€uschtes Gesicht" aria-label="Emoji: EnttĂ€uschtes Gesicht"> Here& #39;s a recent interesting one that I would love to see on transformers...)
Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context
Urvashi Khandelwal, He He, Peng Qi, Dan Jurafsky
(I couldn& #39;t find a classic here
Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context
Urvashi Khandelwal, He He, Peng Qi, Dan Jurafsky
Finally, read Yejin Choi. I tried to add one of her papers, but her work really transcends these categories.
