Nlp Highlights

ACL 2016 outstanding paper, by Vered Shwartz, Yoav Goldberg and Ido Dagan. Waleed presents this paper, discussing hypernymy detection and the methods used in the paper. It's pretty similar to work in relation extraction and knowledge base completion, so we also talk a bit about connections to other methods we're familiar with. Encoding paths using an RNN like they do (and like Arvind Neelakantan did for KBC) improves recall substantially, at the cost of some precision, which makes intuitive sense. https://www.semanticscholar.org/paper/Improving-Hypernymy-Detection-with-an-Integrated-P-Shwartz-Goldberg/05d28e891fd70d123c46ceeb0cdfc0a2cb0d88db

ACL 2017 paper by Abigail See, Peter Liu, and Chris Manning. Matt presents the paper, describing the task (summarization on CNN/Daily Mail), the model (the standard copy + generate model that people are using these days, plus a nice coverage loss term), and the results (can't beat the extractive baseline, but coming close). It's a nice paper - very well written, interesting discussion section. https://www.semanticscholar.org/paper/Get-To-The-Point-Summarization-with-Pointer-Genera-See-Liu/13db673d09f546698e0bfb6687beeb5345f81ad9 Abigail also has a very nice blog post where she describes her work in a less formal tone than the paper: http://www.abigailsee.com/2017/04/16/taming-rnns-for-better-summarization.html

An interview with Noah Smith. Noah tells us about his work with his students Hao Peng and Sam Thomson. We talk about what semantic dependency parsing is, the model that they used to approach the problem, how multi-task learning fits into this with a graph-based parser, and end with a little discussion about representation learning. https://www.semanticscholar.org/paper/Deep-Multitask-Learning-for-Semantic-Dependency-Pa-Peng-Thomson/406fd41b360bb02c0aaabff54055193fb5d9d7f1

https://arxiv.org/abs/1705.10301 Maruan, Avinava Dubey and Eric Xing essentially put the post-hoc decision boundary explanations from the "Why Should I Trust You?" paper* as a core component of a predictive model. Maruan comes on to tell us about it. * https://www.semanticscholar.org/paper/Why-Should-I-Trust-You-Explaining-the-Predictions-Ribeiro-Singh/5636dca44384240ce9aff2b10b78458cd3c2f450

The recently-hyped paper that got "superhuman" performance on FAIR's CLEVR dataset. https://arxiv.org/abs/1706.01427

An interview with Tim Rocktäschel. https://arxiv.org/abs/1705.11040

https://www.semanticscholar.org/paper/Generalizing-to-Unseen-Entities-and-Entity-Pairs-w-Verga-Neelakantan/7dd8b958632b07e41979337c71d847a3f39df456

https://arxiv.org/abs/1705.07962

https://www.semanticscholar.org/paper/Arc-swift-A-Novel-Transition-System-for-Dependency-Qi-Manning/56fc1372a41a46f777ac77859219bb4b76bfd098

http://distill.pub/2016/augmented-rnns/

https://arxiv.org/abs/1705.00557

https://www.semanticscholar.org/paper/Question-Answering-from-Unstructured-Text-by-Retri-Watanabe-Dhingra/89d06c2996f379c602e64b4243f026cd164400d7

https://arxiv.org/abs/1705.02364

https://www.semanticscholar.org/paper/Relation-Extraction-with-Matrix-Factorization-and-Riedel-Yao/52b5eab895a2d9ae24ea72ca72782974a52f90c4

https://www.semanticscholar.org/paper/A-Syntactic-Neural-Model-for-General-Purpose-Code-Yin-Neubig/c8d0e13de2eaa09a928eff36b99d63f494c2f5ec

https://www.semanticscholar.org/paper/Learning-to-Generate-Reviews-and-Discovering-Senti-Radford-Jozefowicz/664ec878de4b7170712baae4a7821fc2602bba25 https://blog.openai.com/unsupervised-sentiment-neuron/

https://www.semanticscholar.org/paper/Finding-News-Citations-for-Wikipedia-Fetahu-Markert/526acf565190d843758b89d37acf281639cb90e2

https://www.semanticscholar.org/paper/Capturing-Semantic-Similarity-for-Entity-Linking-w-Francis-Landau-Durrett/1c9aca60f7ac5edcceb73d612806704a7d662643

https://www.semanticscholar.org/paper/Design-Challenges-for-Entity-Linking-Ling-Singh/aa2a7ac7bfa9a0201d4faddd4e7bb26299a5e0be

https://www.semanticscholar.org/paper/Transition-Based-Dependency-Parsing-with-Stack-Lon-Dyer-Ballesteros/396b7932beac62a72288eaea047981cc9a21379a