Awesome Podcast

NLP Playlist

nlp highlights playlist

In this episode we briefly say what we're up to with the podcast.  No technical content, just a description of what each episode will look like, and why we're doing this.

00 - Intro to the podcast

https://www.semanticscholar.org/paper/A-Comparative-Study-of-Word-Embeddings-for-Reading-Dhingra-Liu/3ec37205c9201fc891ab51da200e361fdc34bfb3

01 - A Comparative Study of Word Embeddings for Reading Comprehension

https://www.semanticscholar.org/paper/Bidirectional-Attention-Flow-for-Machine-Seo-Kembhavi/007ab5528b3bd310a80d553cccad4b78dc496b02

02 - Bidirectional Attention Flow for Machine Comprehension

https://www.semanticscholar.org/paper/FastQA-A-Simple-and-Efficient-Neural-Architecture-Weissenborn-Wiese/7c1576b96a1e246d77b30f7b80cec63be96fa698

03 - FastQA: A Simple and Efficient Neural Architecture for Question Answering

An interview with Chris Dyer.

https://www.semanticscholar.org/paper/Recurrent-Neural-Network-Grammars-Dyer-Kuncoro/1594d954abc650bce2db445c52a76e49655efb0c

04 - Recurrent Neural Network Grammars, with Chris Dyer

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

10 - A Syntactic Neural Model for General-Purpose Code Generation

12 - Supervised Learning of Universal Sentence Representations from Natural Language Inference Data

14 - Discourse-Based Objectives for Fast Unsupervised Sentence Representation Learning

15 - Attention and Augmented Recurrent Neural Networks

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

21 - Contextual Explanation Networks, with Maruan Al-Shedivat

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

22 -  Deep Multitask Learning for Semantic Dependency Parsing, with Noah Smith

ICLR 2017 paper, by Yoon Kim, Carl Denton, Luong Hoang, and Sasha Rush.

Yoon comes on to talk with us about his paper.  The paper shows how standard attentions can be seen as an expected feature count computation, and can be generalized to other kinds of expected feature counts, as long as we have efficient, differentiable algorithms for computing those marginals, like the forward-backward and inside-outside algorithms.  We talk with Yoon about how this works, the experiments they ran to test this idea, and interesting implications of their work.

https://www.semanticscholar.org/paper/Structured-Attention-Networks-Kim-Denton/0aec1745d0e054e8d86d21b20d0ee5fc0d932a49

Yoon also brought up a more recent paper by Yang Liu and Mirella Lapata that computes a very similar kind of structured attention, but does so much more efficiently.  That paper is here: https://www.semanticscholar.org/paper/Learning-Structured-Text-Representations-Liu-Lapata/4435c3586364e8f8a2c8c9ee671c39d7df7e196c.

26 - Structured Attention Networks, with Yoon Kim

NIPS 2016 paper by Alexander Ratner and coauthors in Chris Ré's group at Stanford, presented by Waleed.

The paper presents a method for generating labels for an unlabeled dataset by combining a number of weak labelers.  This changes the annotation effort from looking at individual examples to constructing a large number of noisy labeling heuristics, a task the authors call "data programming".  Then you learn a model that intelligently aggregates information from the weak labelers to create a weighted "supervised" training set.  We talk about this method, how it works, how it's related to ideas like co-training, and when you might want to use it.

https://www.semanticscholar.org/paper/Data-Programming-Creating-Large-Training-Sets-Quic-Ratner-Sa/37acbbbcfe9d8eb89e5b01da28dac6d44c3903ee

28 - Data Programming: Creating Large Training Sets, Quickly

ACL 2017 paper, by Yusuke Oda and others (including Graham Neubig) at Nara Institute of Science and Technology (Graham is now at Carnegie Mellon University).

Graham comes on to talk to us about neural machine translation generally, and about this ACL paper in particular.  We spend the first half of the episode talking about major milestones in neural machine translation and why it is so much more effective than previous methods (spoiler: stronger language models help a lot).  We then talk about the specifics of binary code prediction, how it's related to a hierarchical or class-factored softmax, and how to make it robust to off-by-one-bit errors.

Paper link: https://www.semanticscholar.org/paper/Neural-Machine-Translation-via-Binary-Code-Predict-Oda-Arthur/bbedfd0380eb2e62f1c3b61aaf484d5867e6358d

An example of the Language log posts that we discussed: http://languagelog.ldc.upenn.edu/nll/?p=33613 (there are many more).

29 - Neural machine translation via binary code prediction, with Graham Neubig

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 or may not be useful. We discuss imitation learning, roll-in and roll-out, and how to approximate an expert with a reference policy. 

DAgger: https://www.semanticscholar.org/paper/A-Reduction-of-Imitation-Learning-and-Structured-to-Ross-Gordon/17eddf33b513ae1134abadab728bdbf6abab2a05?navId=citing-papers
RESLOPE: http://legacydirs.umiacs.umd.edu/~hal/docs/daume18reslope.pdf

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

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 words in a sentence approximates the number of hops between them in the dependency tree. The proposed method shows that ELMo and BERT representations, trained with no syntactic supervision, embed many of the unlabeled, undirected dependency attachments between words in the same sentence.

Paper: https://nlp.stanford.edu/pubs/hewitt2019structural.pdf
GitHub repository: https://github.com/john-hewitt/structural-probes
Blog post: https://nlp.stanford.edu/~johnhew/structural-probe.html
Twitter thread: https://twitter.com/johnhewtt/status/1114252302141886464
John's homepage: https://nlp.stanford.edu/~johnhew/

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

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 transfer models across domains, and the different ways used to evaluate these systems. Zhou also shares her thoughts on exciting future directions such as developing dialogue methods for non-cooperative environments (e.g., to negotiate prices) and multimodal dialogue systems (e.g., using video as well as audio/text).

Zhou Yu's homepage: http://zhouyu.cs.ucdavis.edu/

89 - Dialog Systems, with Zhou Yu

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 about the main components of a semantic parser, how the formal language affects design choices in the parser, and end with a discussion of some exciting open problems in this space.

Jonathan Berant's homepage: http://www.cs.tau.ac.il/~joberant/

91 - (Executable) Semantic Parsing, with Jonathan Berant

In this episode, we invite David Bamman to give an overview of computational humanities. We discuss examples of questions studied in computational humanities (e.g., characterizing fictionality, assessing novelty, measuring the attention given to male vs. female characters in the literature). We talk about the role NLP plays in addressing these questions and how the accuracy and biases of NLP models can influence the results. We also discuss understudied NLP tasks which can help us answer more questions in this domain such as literary scene coreference resolution and constructing a map of literature geography.

David Bamman's homepage: http://people.ischool.berkeley.edu/~dbamman/
LitBank dataset: https://github.com/dbamman/litbank

92 - Computational Humanities, with David Bamman

**The podcast is currently on hiatus. For more active NLP content, check out the Holistic Intelligence Podcast linked below.**

Welcome to the NLP highlights podcast, where we invite researchers to talk about their work in various areas in natural language processing. All views expressed belong to the hosts/guests, and do not represent their employers.

21 - Contextual Explanation Networks, with Maruan Al-Shedivat

NLP Highlights