just like that ;)! .. "EFL students' atttitudes, strategies and perceptions about Machine Translation and esp. the problems they face when using these machine translation tools.."  Sounds like a nice area to do more research for my "Technology and Foreign Language Education" doctoral course :)

NLP Playlist

In this special episode, we chatted with Chris Callison-Burch about his testimony in the recent U.S. Congress Hearing on the Interoperability of AI and Copyright Law. We started by asking Chris about the purpose and the structure of this hearing. Then we talked about the ongoing discussion on how the copyright law is applicable to content generated by AI systems, the potential risks generative AI poses to artists, and Chris’ take on all of this. We end the episode with a recording of Chris’ opening statement at the hearing.

140 - Generative AI and Copyright, with Chris Callison-Burch

In this special episode of NLP Highlights, we discussed building and open sourcing language models. What is the usual recipe for building large language models? What does it mean to open source them? What new research questions can we answer by open sourcing them? We particularly focused on the ongoing Open Language Model (OLMo) project at AI2, and invited Iz Beltagy and Dirk Groeneveld, the research and engineering leads of the OLMo project to chat.

Blog post announcing OLMo: https://blog.allenai.org/announcing-ai2-olmo-an-open-language-model-made-by-scientists-for-scientists-ab761e4e9b76

Organizations interested in partnership can express their interest here: https://share.hsforms.com/1blFWEWJ2SsysSXFUEJsxuA3ioxm

You can find Iz at twitter.com/i_beltagy and Dirk at twitter.com/mechanicaldirk

141 - Building an open source LM, with Iz Beltagy and Dirk Groeneveld

Our first guest with this new format is Kyle Lo, the most senior lead scientist in the Semantic Scholar team at Allen Institute for AI (AI2), who kindly agreed to share his perspective on #Science of #Science (#scisci) on our podcast. SciSci is concerned with studying how people do science, and includes developing methods and tools to help people consume AND produce science. Kyle has made several critical contributions in this field which enabled a lot of SciSci work over the past 5+ years, ranging from novel NLP methods (eg, SciBERT https://lnkd.in/gTP_tYiF ), to open data collections (eg, S2ORK https://lnkd.in/g4J6tXCG), to toolkits for manipulating scientific documents (eg, PaperMage https://lnkd.in/gwU7k6mJ which JUST received a Best Paper Award 🏆 at EMNLP 2023).

Kyle Lo's homepage: https://kyleclo.github.io/

142 - Science Of Science, with Kyle Lo

This podcast episode features Dr. Mohamed Elhoseiny, a true luminary in the realm of computer vision with over a decade of groundbreaking research. As an Assistant Professor at KAUST, Dr. Elhoseiny's work delves into the intersections of Computer Vision, Language & Vision, and Computational Creativity in Art, Fashion, and AI. Notably, he co-organized the 1st and 2nd Workshops on Closing the Loop between Vision and Language, demonstrating his commitment to advancing interdisciplinary research. With a rich educational background from Stanford University's Graduate School of Business Ignite Program, and Rutgers University as MS/PhD Researcher, coupled with influential stints at Stanford, Baidu Research, Facebook AI Research, Adobe Research, and SRI International, Dr. Elhoseiny brings a wealth of experience to our discussion.

"Imaginative AI" with Mohamed Elhoseiny

Curious about the safety of LLMs? 🤔 Join us for an insightful new episode featuring Suchin Gururangan, Young Investigator at Allen Institute for Artificial Intelligence and Data Science Engineer at Appuri. 🚀 Don't miss out on expert insights into the world of LLMs!

Are LLMs safe?

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

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

**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.

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

NLP Highlights

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