NLP Highlights

By Recent papers discussed by Matt and Waleed

Science & Medicine

What's NLP Highlights about?

Discussing recent and interesting work related to natural language processing. Matt Gardner and Waleed Ammar, research scientists at the Allen Institute for Artificial Intelligence, give short discussions of papers, mostly in interviews with authors about their work.

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(Executable) Semantic Parsi...

06.26.2019

In this episode, we invite Jonathan Berant to talk about executable semantic parsing. We discuss what executable semantic parsing is and how it differs from related tasks such as semantic dependency parsing and abstract meaning representation (AMR) parsing. We talk ...

NLP Highlights episodes:

06.26.2019 (Executable) Semantic Parsing, with Jonathan Berant In this episode, we invite Jonathan Berant to talk about executable semantic parsing. We discuss what executable semantic parsing is and how it differs from related tasks such as semantic dependency parsing and abstract meaning representation (AMR) parsing. We talk ...
05.31.2019 90 - Research in Academia versus Industry, with Philip Resnik and Jason Baldridge How is it like to do research in academia vs. industry? In this episode, we invite Jason Baldridge (UT Austin => Google) and Philip Resnik (Sun Microsystems => UMD) to discuss some of the aspects one may want to consider ...
05.31.2019 89 - Dialog Systems, with Zhou Yu In this episode, we invite Zhou Yu to give an overview of dialogue systems. We discuss different types of dialogue systems (task-oriented vs. non-task-oriented), the main building blocks and how they relate to other research areas in NLP, how to ...
05.07.2019 88 - A Structural Probe for Finding Syntax in Word Representations, with John Hewitt In this episode, we invite John Hewitt to discuss his take on how to probe word embeddings for syntactic information. The basic idea is to project word embeddings to a vector space where the L2 distance between a pair of ...
04.25.2019 87 - Pathologies of Neural Models Make Interpretation Difficult, with Shi Feng In this episode, Shi Feng joins us to discuss his recent work on identifying pathological behaviors of neural models for NLP tasks. Shi uses input word gradients to identify the least important word for a model's prediction, and iteratively removes ...
04.15.2019 86 - NLP for Evidence-based Medicine, with Byron Wallace In this episode, Byron Wallace tells us about interdisciplinary work between evidence based medicine and natural language processing. We discuss extracting PICO frames from articles describing clinical trials and data available for direct and weak supervision. We also discuss automating ...
03.29.2019 85 - Stress in Research, with Charles Sutton In this episode, Charles Sutton walks us through common sources of stress for researchers and suggests coping strategies to maintain your sanity. We talk about how pursuing a research career is similar to participating in a life-long international tournament, conflating ...
03.26.2019 84 - Large Teams Develop, Small Groups Disrupt, with Lingfei Wu In a recent Nature paper, Lingfei Wu (Ling) suggests that smaller teams of scientists tend to do more disruptive work. In this episode, we invite Ling to discuss their results, how they define disruption and possible reasons why smaller teams ...
03.13.2019 83 - Knowledge Base Construction, with Sebastian Riedel In this episode, we invite Sebastian Riedel to talk about knowledge base construction (KBC). Why is it an important research area? What are the tradeoffs between using an open vs. closed schema? What are popular methods currently used, and what ...
03.06.2019 82 - Visual Reasoning, with Yoav Artzi In this episode, Yoav Artzi joins us to talk about visual reasoning. We start by defining what visual reasoning is, then discuss the pros and cons of different tasks and datasets. We discuss some of the models used for visual ...
02.06.2019 81 - BlackboxNLP, with Afra Alishahi and Tal Linzen Neural models recently resulted in large performance improvements in various NLP problems, but our understanding of what and how the models learn remains fairly limited. In this episode, Tal Linzen and Afra Alishahi talk to us about BlackboxNLP, an EMNLP’18 ...
01.29.2019 80 - Leaderboards and Science, with Siva Reddy Originally used to entice fierce competitions in arcade games, leaderboards recently made their way into NLP research circles. Leaderboards could help mitigate some of the problems in how researchers run experiments and share results (e.g., accidentally overfitting models on a ...
01.21.2019 79 - The glass ceiling in NLP, with Natalie Schluter In this episode, Natalie Schluter talks to us about a data-driven analysis of career progression of male vs. female researchers in NLP through the lens of mentor-mentee networks based on ~20K papers in the ACL anthology. Directed edges in the ...
01.15.2019 78. Where do corpora come from?, with Matt Honnibal and Ines Montani Most NLP projects rely crucially on the quality of annotations used for training and evaluating models. In this episode, Matt and Ines of Explosion AI tell us how Prodigy can improve data annotation and model development workflows. Prodigy is an ...
01.07.2019 77. On Writing Quality Peer Reviews, with Noah A. Smith It's not uncommon for authors to be frustrated with the quality of peer reviews they receive in (NLP) conferences. In this episode, Noah A. Smith shares his advice on how to write good peer reviews. The structure Noah recommends for ...
11.27.2018 76 - Increasing In-Class Similarity by Retrofitting Embeddings with Demographics, with Dirk Hovy EMNLP 2018 paper by Dirk Hovy and Tommaso Fornaciari. https://www.semanticscholar.org/paper/Improving-Author-Attribute-Prediction-by-Linguistic-Hovy-Fornaciari/71aad8919c864f73108aafd8e926d44e9df51615 In this episode, Dirk Hovy talks about natural language as social phenomenon which can provide insights about those who generate it. For example, this paper uses retrofitted embeddings to improve ...
11.21.2018 75 - Reinforcement / Imitation Learning in NLP, with Hal Daumé III In this episode, we invite Hal Daumé to continue the discussion on reinforcement learning, focusing on how it has been used in NLP. We discuss how to reduce NLP problems into the reinforcement learning framework, and circumstances where it may ...
11.16.2018 74 - Deep Reinforcement Learning Doesn't Work Yet, with Alex Irpan Blog post by Alex Irpan titled "Deep Reinforcement Learning Doesn't Work Yet" https://www.alexirpan.com/2018/02/14/rl-hard.html In this episode, Alex Irpan talks about limitations of current deep reinforcement learning methods and why we have a long way to go before they go mainstream. ...
11.13.2018 73 - Supersense Disambiguation of English Prepositions and Possessives, with Nathan Schneider ACL 2018 paper by Nathan Schneider, Jena D. Hwang, Vivek Srikumar, Jakob Prange, Austin Blodgett, Sarah R. Moeller, Aviram Stern, Adi Bitan, Omri Abend. In this episode, Nathan discusses how the meaning of prepositions varies, proposes a hierarchy for classifying ...
10.16.2018 72 - The Anatomy Question Answering Task, with Jordan Boyd-Graber Our first episode in a new format: broader surveys of areas, instead of specific discussions on individual papers. In this episode, we talk with Jordan Boyd-Graber about question answering. Matt starts the discussion by giving five different axes on which ...
10.12.2018 71 - DuoRC: Complex Language Understanding with Paraphrased Reading Comprehension, with Amrita Saha ACL 2018 paper by Amrita Saha, Rahul Aralikatte, Mitesh M. Khapra, Karthik Sankaranarayanan Amrita and colleagues at IBM Research introduced a harder dataset for "reading comprehension", where you have to answer questions about a given passage of text. Amrita joins ...
09.18.2018 70 - Measuring the Evolution of a Scientific Field through Citation Frames, with David Jurgens TACL 2018 paper (presented at ACL 2018) by David Jurgens, Srijan Kumar, Raine Hoover, Daniel A. McFarland, and Daniel Jurafsky David comes on the podcast to talk to us about citation frames. We discuss the dataset they created by painstakingly ...
09.10.2018 69 - Second language acquisition modeling, with Burr Settles A shared task held in conjunction with a NAACL 2018 workshop, organized by Burr Settles and collaborators at Duolingo. Burr tells us about the shared task. The goal of the task was to predict errors that a language learner would ...
09.04.2018 68 - Neural models of factuality, with Rachel Rudinger NAACL 2018 paper, by Rachel Rudinger, Aaron Steven White, and Benjamin Van Durme Rachel comes on to the podcast, telling us about what factuality is (did an event happen?), what datasets exist for doing this task (a few; they made ...
08.27.2018 67 - GLUE: A Multi-Task Benchmark and Analysis Platform, with Sam Bowman Paper by Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. Sam comes on to tell us about GLUE. We talk about the motivation behind setting up a benchmark framework for natural language understanding, how ...
08.20.2018 66 - Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods, with Jieyu Zhao NACL 2018 paper, by Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. Jieyu comes on the podcast to talk about bias in coreference resolution models. This bias makes models rely disproportionately on gender when making decisions for ...
08.13.2018 65 - Event Representations with Tensor-based Compositions, with Niranjan Balasubramanian AAAI 2018 paper by Noah Weber, Niranjan Balasubramanian, and Nathanael Chambers Niranjan joins us on the podcast to tell us about his latest contribution in a line of work going back to Shank's scripts. This work tries to model sequences ...
08.08.2018 64 - Neural Network Models for Sentence Pair Tasks, with Wuwei Lan and Wei Xu Best reproduction paper at COLING 2018, by Wuwei Lan and Wei Xu. This paper takes a bunch of models for sentence pair classification (including paraphrase identification, semantic textual similarity, natural language inference / entailment, and answer sentence selection for QA) ...
08.02.2018 63 - Neural Lattice Language Models, with Jacob Buckman TACL 2018 paper by Jacob Buckman and Graham Neubig. Jacob tells us about marginalizing over latent structure in a sentence by doing a clever parameterization of a lattice with a model kind of like a tree LSTM. This lets you ...
07.30.2018 62 - Sounding Board: A User-Centric and Content-Driven Social Chatbot, with Hao Fang NAACL 2018 demo paper, by Hao Fang, Hao Cheng, Maarten Sap, Elizabeth Clark, Ari Holtzman, Yejin Choi, Noah A. Smith, and Mari Ostendorf Sounding Board was the system that won the 2017 Amazon Alexa Prize, a competition to build a ...
07.23.2018 61 - Neural Text Generation in Stories, with Elizabeth Clark and Yangfeng Ji NAACL 2018 Outstanding Paper by Elizabeth Clark, Yangfeng Ji, and Noah A. Smith Both Elizabeth and Yangfeng come on the podcast to tell us about their work. This paper is an extension of an EMNLP 2017 paper by Yangfeng and ...
06.28.2018 60 - FEVER: a large-scale dataset for Fact Extraction and VERification, with James Thorne NAACL 2018 paper by James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal James tells us about his paper, where they created a dataset for fact checking. We talk about how this dataset relates to other datasets, why a new ...
06.12.2018 59 - Weakly Supervised Semantic Parsing With Abstract Examples, with Omer Goldman ACL 2018 paper by Omer Goldman, Veronica Latcinnik, Udi Naveh, Amir Globerson, and Jonathan Berant Omer comes on to tell us about a class project (done mostly by undergraduates!) that made it into ACL. Omer and colleagues built a semantic ...
06.08.2018 58 - Learning What’s Easy: Fully Differentiable Neural Easy-First Taggers, with André Martins EMNLP 2017 paper by André F. T. Martins and Julia Kreutzer André comes on the podcast to talk to us the paper. We spend the bulk of the time talking about the two main contributions of the paper: how they ...
06.05.2018 57 - A Survey Of Cross-lingual Word Embedding Models, with Sebastian Ruder Upcoming JAIR paper by Sebastian Ruder, Ivan Vulić, and Anders Søgaard. Sebastian comes on to tell us about his survey. He creates a typology of cross-lingual word embedding methods, and we discuss why you might use cross-lingual embeddings (low-resource languages ...
04.04.2018 56 - Deep contextualized word representations, with Matthew Peters NAACL 2018 paper, by Matt Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Chris Clark, Kenton Lee, and Luke Zettlemoyer. In this episode, AI2's own Matt Peters comes on the show to talk about his recent work on ELMo embeddings, what ...
03.28.2018 55 - Matchbox: Dispatch-driven autobatching for imperative deep learning, with James Bradbury In this episode, we take a more systems-oriented approach to NLP, looking at issues with writing deep learning code for NLP models. As a lot of people have discovered over the last few years, efficiently batching multiple examples together for ...
03.26.2018 54 - Simulating Action Dynamics with Neural Process Networks, with Antoine Bosselut ICLR 2018 paper, by Antoine Bosselut, Omer Levy, Ari Holtzman, Corin Ennis, Dieter Fox, and Yejin Choi. This is not your standard NLP task. This work tries to predict which entities change state over the course of a recipe (e.g., ...
03.21.2018 53 - Classical Structured Prediction Losses for Sequence to Sequence Learning, with Sergey and Myle NAACL 2018 paper, by Sergey Edunov, Myle Ott, Michael Auli, David Grangier, and Marc'Aurelio Ranzato, from Facebook AI Research In this episode we continue our theme from last episode on structured prediction, talking with Sergey and Myle about their paper. ...
03.15.2018 52 - Sequence-to-Sequence Learning as Beam-Search Optimization, with Sam Wiseman EMNLP 2016 paper by Sam Wiseman and Sasha Rush. In this episode we talk with Sam about a paper from a couple of years ago on bringing back some ideas from structured prediction into neural seq2seq models. We talk about ...
03.12.2018 51 - A Regularized Framework for Sparse and Structured Neural Attention, with Vlad Niculae NIPS 2017 paper by Vlad Niculae and Mathieu Blondel. Vlad comes on to tell us about his paper. Attentions are often computed in neural networks using a softmax operator, which maps scalar outputs from a model into a probability space ...
02.14.2018 50 - Cardinal Virtues: Extracting Relation Cardinalities from Text, with Paramita Mirza ACL 2017 paper, by Paramita Mirza, Simon Razniewski, Fariz Darari, and Gerhard Weikum. There's not a whole lot of work on numbers in NLP, and getting good information out of numbers expressed in text can be challenging. In this episode, ...
02.05.2018 49 - A Joint Sequential and Relational Model for Frame-Semantic Parsing, with Bishan Yang EMNLP 2017 paper by Bishan Yang and Tom Mitchell. Bishan tells us about her experiments on frame-semantic parsing / semantic role labeling, which is trying to recover the predicate-argument structure from natural language sentences, as well as categorize those structures ...
01.29.2018 48 - Incidental Supervision: Moving Beyond Supervised Learning, with Dan Roth AAAI 2017 paper, by Dan Roth. In this episode we have a conversation with Dan about what he means by "incidental supervision", and how it's related to ideas in reinforcement learning and representation learning. For many tasks, there are signals ...
01.24.2018 47 - Dynamic integration of background knowledge in neural NLU systems, with Dirk Weißenborn How should you incorporate background knowledge into a neural net? A lot of people have been thinking about this problem, and Dirk Weissenborn comes on to tell us about his work in this area. Paper is with Tomáš Kočiský and ...
01.08.2018 46 - Parsing with Traces, with Jonathan Kummerfeld TACL 2017 paper by Jonathan K. Kummerfeld and Dan Klein. Jonathan tells us about his work on parsing algorithms that capture traces and null elements in sentence structure. We spend the first third of the conversation talking about what these ...
01.02.2018 45 - Build It, Break It workshop, with Allyson Ettinger and Sudha Rao How robust is your NLP system? High numbers on common datasets can be misleading, as most systems are easily fooled by small modifications that would not be hard for humans to understand. Allyson Ettinger, Sudha Rao, Hal Daumé III, and ...
12.07.2017 44 - Truly Low Resource NLP, with Anders Søgaard Anders talks with us about his line of work on doing NLP in languages where you have no linguistic resources other than a Bible translation or other religious works. He and his students have developed methods for annotation projection for ...
12.04.2017 43 - Reinforced Video Captioning with Entailment Rewards, with Ramakanth and Mohit EMNLP 2017 paper by Ramakanth Pasunuru and Mohit Bansal Ram and Mohit join us to talk about their work, which uses reinforcement learning to improve performance on a video captioning task. They directly optimize CIDEr, a popular image/video captioning metric, ...
11.30.2017 42 - Generating Sentences by Editing Prototypes, with Kelvin Guu Paper is by Kelvin Guu, Tatsunori B. Hashimoto, Yonatan Oren, and Percy Liang In this episode, Kelvin tells us how to build a language model that starts from a prototype sentence instead of starting from scratch, enabling much more grammatical ...

Older episodes

NLP Highlights

What's NLP Highlights about?

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NLP Highlights episodes:

(Executable) Semantic Parsing, with Jonathan Berant

90 - Research in Academia versus Industry, with Philip Resnik and Jason Baldridge

89 - Dialog Systems, with Zhou Yu

88 - A Structural Probe for Finding Syntax in Word Representations, with John Hewitt

87 - Pathologies of Neural Models Make Interpretation Difficult, with Shi Feng

86 - NLP for Evidence-based Medicine, with Byron Wallace

85 - Stress in Research, with Charles Sutton

84 - Large Teams Develop, Small Groups Disrupt, with Lingfei Wu

83 - Knowledge Base Construction, with Sebastian Riedel

82 - Visual Reasoning, with Yoav Artzi

81 - BlackboxNLP, with Afra Alishahi and Tal Linzen

80 - Leaderboards and Science, with Siva Reddy

79 - The glass ceiling in NLP, with Natalie Schluter

78. Where do corpora come from?, with Matt Honnibal and Ines Montani

77. On Writing Quality Peer Reviews, with Noah A. Smith

76 - Increasing In-Class Similarity by Retrofitting Embeddings with Demographics, with Dirk Hovy

75 - Reinforcement / Imitation Learning in NLP, with Hal Daumé III

74 - Deep Reinforcement Learning Doesn't Work Yet, with Alex Irpan

73 - Supersense Disambiguation of English Prepositions and Possessives, with Nathan Schneider

72 - The Anatomy Question Answering Task, with Jordan Boyd-Graber

71 - DuoRC: Complex Language Understanding with Paraphrased Reading Comprehension, with Amrita Saha

70 - Measuring the Evolution of a Scientific Field through Citation Frames, with David Jurgens

69 - Second language acquisition modeling, with Burr Settles

68 - Neural models of factuality, with Rachel Rudinger

67 - GLUE: A Multi-Task Benchmark and Analysis Platform, with Sam Bowman

66 - Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods, with Jieyu Zhao

65 - Event Representations with Tensor-based Compositions, with Niranjan Balasubramanian

64 - Neural Network Models for Sentence Pair Tasks, with Wuwei Lan and Wei Xu

63 - Neural Lattice Language Models, with Jacob Buckman

62 - Sounding Board: A User-Centric and Content-Driven Social Chatbot, with Hao Fang

61 - Neural Text Generation in Stories, with Elizabeth Clark and Yangfeng Ji

60 - FEVER: a large-scale dataset for Fact Extraction and VERification, with James Thorne

59 - Weakly Supervised Semantic Parsing With Abstract Examples, with Omer Goldman

58 - Learning What’s Easy: Fully Differentiable Neural Easy-First Taggers, with André Martins

57 - A Survey Of Cross-lingual Word Embedding Models, with Sebastian Ruder

56 - Deep contextualized word representations, with Matthew Peters

55 - Matchbox: Dispatch-driven autobatching for imperative deep learning, with James Bradbury

54 - Simulating Action Dynamics with Neural Process Networks, with Antoine Bosselut

53 - Classical Structured Prediction Losses for Sequence to Sequence Learning, with Sergey and Myle

52 - Sequence-to-Sequence Learning as Beam-Search Optimization, with Sam Wiseman

51 - A Regularized Framework for Sparse and Structured Neural Attention, with Vlad Niculae

50 - Cardinal Virtues: Extracting Relation Cardinalities from Text, with Paramita Mirza

49 - A Joint Sequential and Relational Model for Frame-Semantic Parsing, with Bishan Yang

48 - Incidental Supervision: Moving Beyond Supervised Learning, with Dan Roth

47 - Dynamic integration of background knowledge in neural NLU systems, with Dirk Weißenborn

46 - Parsing with Traces, with Jonathan Kummerfeld

45 - Build It, Break It workshop, with Allyson Ettinger and Sudha Rao

44 - Truly Low Resource NLP, with Anders Søgaard

43 - Reinforced Video Captioning with Entailment Rewards, with Ramakanth and Mohit

42 - Generating Sentences by Editing Prototypes, with Kelvin Guu

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