We invited Jayant Krishnamurthy and Hao Fang, researchers at Microsoft Semantic Machines to discuss their platform for building task-oriented dialog systems, and their recent TACL paper on the topic. The paper introduces a new formalism for task-oriented dialog to effectively handle references and revisions in complex dialog, and a large realistic dataset that uses this formalism.

Leaderboard associated with the dataset: https://microsoft.github.io/task_oriented_dialogue_as_dataflow_synthesis/
Jayant's Twitter handle: https://twitter.com/jayantkrish
Hao's Twitter handle: https://twitter.com/hfang90

124 - Semantic Machines and Task-Oriented Dialog, with Jayant Krishnamurthy and Hao Fang

We invited Lisa Li to talk about her recent work, Prefix-Tuning: Optimizing Continuous Prompts for Generation. Prefix tuning is a lightweight alternative to finetuning, and the idea is to tune only a fixed-length task-specific continuous vector, and to keep the pretrained transformer parameters frozen. We discussed how prefix tuning compares with finetuning and other efficient alternatives on two tasks in various experimental settings, and in what scenarios prefix tuning is preferable.

Lisa is a Phd student at Stanford University. Lisa's webpage: https://xiangli1999.github.io/

The hosts for this episode are Pradeep Dasigi and Ana Marasović.

126 - Optimizing Continuous Prompts for Generation, with Lisa Li

We discussed adversarial dataset construction and dynamic benchmarking in this episode with Douwe Kiela, a research scientist at Facebook AI Research who has been working on a dynamic benchmarking platform called Dynabench. Dynamic benchmarking tries to address the issue of many recent datasets getting solved with little progress being made towards solving the corresponding tasks. The idea is to involve models in the data collection loop to encourage humans to provide data points that are hard for those models, thereby continuously collecting harder datasets. We discussed the details of this approach, and some potential caveats. We also discussed dynamic leaderboards, a recent addition to Dynabench that rank systems based on their utility given specific use cases.

Papers discussed in this episode:
1. Dynabench: Rethinking Benchmarking in NLP (https://www.semanticscholar.org/paper/Dynabench%3A-Rethinking-Benchmarking-in-NLP-Kiela-Bartolo/77a096d80eb4dd4ccd103d1660c5a5498f7d026b)
2. Dynaboard: An Evaluation-As-A-Service Platform for Holistic Next-Generation Benchmarking (https://www.semanticscholar.org/paper/Dynaboard%3A-An-Evaluation-As-A-Service-Platform-for-Ma-Ethayarajh/d25bb256e5b69f769a429750217b0d9ec1cf4d86)
3. Adversarial NLI: A New Benchmark for Natural Language Understanding (https://www.semanticscholar.org/paper/Adversarial-NLI%3A-A-New-Benchmark-for-Natural-Nie-Williams/9d87300892911275520a4f7a5e5abf4f1c002fec)
4. DynaSent: A Dynamic Benchmark for Sentiment Analysis (https://www.semanticscholar.org/paper/DynaSent%3A-A-Dynamic-Benchmark-for-Sentiment-Potts-Wu/284dfcf7f25ca87b2db235c6cdc848b4143d3923)

Douwe Kiela's webpage: https://douwekiela.github.io/

The hosts for this episode are Pradeep Dasigi and Alexis Ross.

128 - Dynamic Benchmarking, with Douwe Kiela

In this episode, we talk with Lisa Beinborn, an assistant professor at Vrije Universiteit Amsterdam, about how to use human cognitive signals to improve and analyze NLP models. We start by discussing different kinds of cognitive signals—eye-tracking, EEG, MEG, and fMRI—and challenges associated with using them. We then turn to Lisa’s recent work connecting interpretability measures with eye-tracking data, which reflect the relative importance measures of different tokens in human reading comprehension. We discuss empirical results suggesting that eye-tracking signals correlate strongly with gradient-based saliency measures, but not attention, in NLP methods. We conclude with discussion of the implications of these findings, as well as avenues for future work.

Papers discussed in this episode:
Towards best practices for leveraging human language processing signals for natural language processing: https://api.semanticscholar.org/CorpusID:219309655
Relative Importance in Sentence Processing: https://api.semanticscholar.org/CorpusID:235358922

Lisa Beinborn’s webpage: https://beinborn.eu/

The hosts for this episode are Alexis Ross and Pradeep Dasigi.

130 - Linking human cognitive patterns to NLP Models, with Lisa Beinborn

What can NLP researchers learn from Human Computer Interaction (HCI) research? We chatted with Nanna Inie and Leon Derczynski to find out. We discussed HCI's research processes including methods of inquiry, the data annotation processes used in HCI, and how they are different from NLP, and the cognitive methods used in HCI for qualitative error analyses. We also briefly talked about the opportunities the field of HCI presents for NLP researchers.

This discussion is based on the following paper: https://aclanthology.org/2021.hcinlp-1.16/

Nanna Inie is a postdoctoral researcher and Leon Derczynski is an associate professor in CS at the IT University of Copenhagen.

The hosts for this episode are Ana Marasović and Pradeep Dasigi.

131 - Opportunities and Barriers between HCI and NLP, with Nanna Inie and Leon Derczynski

In this episode, we discussed the Alexa Prize Socialbot Grand Challenge and this year's winning submission, Alquist 4.0, with Petr Marek, a member of the winning team. Petr gave us an overview of their submission, the design choices that led to them winning the competition, including combining a hardcoded dialog tree and a neural generator model and extracting implicit personal information about users from their responses, and some outstanding challenges.

Petr Marek is a PhD student at the Czech Technical University in Prague.

More about the Alexa Prize challenges: https://developer.amazon.com/alexaprize

Technical report on Alquist 4.0: https://arxiv.org/abs/2109.07968

132 - Alexa Prize Socialbot Grand Challenge and Alquist 4.0, with Petr Marek

This episode is the first in our current series on PhD applications.

How should people prepare their applications to PhD programs in NLP? In this episode, we invite Nathan Schneider (Professor of Linguistics and Computer Science at Georgetown University) and Roma Patel (PhD student in Computer Science at Brown University) to share their perspectives on preparing application materials.

We start by talking about what factors should go into the decision to apply for PhD programs and how to gain relevant experience. We then talk about the most important parts of an application, focusing particularly on how to write a strong statement of purpose and choose recommendation letter writers.

Blog posts mentioned in this episode:
- Nathan Schneider’s Advice on Statements of Purpose: https://nschneid.medium.com/inside-ph-d-admissions-what-readers-look-for-in-a-statement-of-purpose-3db4e6081f80
- Student Perspectives on Applying to NLP PhD Programs: https://blog.nelsonliu.me/2019/10/24/student-perspectives-on-applying-to-nlp-phd-programs/

Homepages:
- Nathan Schneider: https://people.cs.georgetown.edu/nschneid/
- Roma Patel: http://cs.brown.edu/people/rpatel59/

The hosts for this episode are Alexis Ross and Nishant Subramani.

133 - PhD Application Series: Preparing Application Materials, with Nathan Schneider and Roma Patel

This episode is the second in our current series on PhD applications.

How do PhD programs in Europe differ from PhD programs in the US, and how should people decide between them? In this episode, we invite Barbara Plank (Professor at ITU, IT University of Copenhagen) and Gonçalo Correia (ELLIS PhD student at University of Lisbon and University of Amsterdam) to share their perspectives on this question.

We start by talking about the main differences between pursuing a PhD in Europe and the US. We then talk about the application requirements for European PhD programs and factors to consider when deciding whether to apply in Europe or the US. We conclude by talking about the ELLIS PhD program, a relatively new program for PhD students that facilitates collaborations across Europe.

ELLIS PhD program: https://ellis.eu/phd-postdoc (Application Deadline: November 15, 2021)

Homepages:
- Barbara Plank: https://bplank.github.io/
- Gonçalo Correia: https://goncalomcorreia.github.io/

The hosts for this episode are Alexis Ross and Zhaofeng Wu.

134 - PhD Application Series: PhDs in Europe versus the US, with Barbara Plank and Gonçalo Correia

This episode is the third in our current series on PhD applications.

We talk about what the PhD application process looks like after applications are submitted. We start with a general overview of the timeline, then talk about how to approach interviews and conversations with faculty, and finish by discussing the different factors to consider in deciding between programs.

The guests for this episode are Rada Mihalcea (Professor at the University of Michigan), Aishwarya Kamath (PhD student at NYU), and Sanjay Subramanian (PhD student at UC Berkeley).

Homepages:
- Aishwarya Kamath: https://ashkamath.github.io/
- Sanjay Subramanian: https://sanjayss34.github.io/
- Rada Mihalcea: https://web.eecs.umich.edu/~mihalcea/

The hosts for this episode are Alexis Ross and Nishant Subramani.

135 - PhD Application Series: After Submitting Applications

In this episode, we talk with Kayo Yin, an incoming PhD at Berkeley, and Malihe Alikhani, an assistant professor at the University of Pittsburgh, about opportunities for the NLP community to contribute to Sign Language Processing (SLP). We talked about history and misconceptions about sign languages, high-level similarities and differences between spoken and sign languages, distinct linguistic features of signed languages, representations, computational resources, SLP tasks, and suggestions for better design and implementation of SLP models.

136 - Including Signed Languages in NLP, with Kayo Yin and Malihe Alikhani

We invited Urvashi Khandelwal, a research scientist at Google Brain to talk about nearest neighbor language and machine translation models. These models interpolate parametric (conditional) language models with non-parametric distributions over the closest values in some data stores built from relevant data. Not only are these models shown to outperform the usual parametric language models, they also have important implications on memorization and generalization in language models.

Urvashi's webpage: https://urvashik.github.io
Papers discussed:
1) Generalization through memorization: Nearest Neighbor Language Models (https://www.semanticscholar.org/paper/7be8c119dbe065c52125ee7716601751f3116844)
2)Nearest Neighbor Machine Translation (https://www.semanticscholar.org/paper/20d51f8e449b59c7e140f7a7eec9ab4d4d6f80ea)

137 - Nearest Neighbor Language Modeling and Machine Translation, with Urvashi Khandelwal

Compositional generalization refers to the capability of models to generalize to out-of-distribution instances by composing information obtained from the training data. In this episode we chatted with Najoung Kim, on how to explicitly evaluate specific kinds of compositional generalization in neural network models of language. Najoung described COGS, a dataset she built for this, some recent results in the space, and why we should be careful about interpreting the results given the current practice of pretraining models of lots of unlabeled text.

Najoung's webpage: https://najoungkim.github.io/

Papers we discussed:
1. COGS: A Compositional Generalization Challenge Based on Semantic Interpretation (Kim et al., 2020): https://www.semanticscholar.org/paper/b20ddcbd239f3fa9acc603736ac2e4416302d074
2. Compositional Generalization Requires Compositional Parsers (Weissenhorn et al., 2022): https://www.semanticscholar.org/paper/557ebd17b7c7ac4e09bd167d7b8909b8d74d1153
3. Uncontrolled Lexical Exposure Leads to Overestimation of Compositional Generalization in Pretrained Models (Kim et al., 2022): https://www.semanticscholar.org/paper/8969ea3d254e149aebcfd1ffc8f46910d7cb160e

Note that we referred to the final paper by an earlier name in the discussion.

138 - Compositional Generalization in Neural Networks, with Najoung Kim

How can we generate coherent long stories from language models? Ensuring that the generated story has long range consistency and that it conforms to a high level plan is typically challenging. In this episode, Kevin Yang describes their system that prompts language models to first generate an outline, and iteratively generate the story while following the outline and reranking and editing the outputs for coherence. We also discussed the challenges involved in evaluating long generated texts.

Kevin Yang is a PhD student at UC Berkeley.

Kevin's webpage: https://people.eecs.berkeley.edu/~yangk/

Papers discussed in this episode:
1. Re3: Generating Longer Stories With Recursive Reprompting and Revision (https://www.semanticscholar.org/paper/Re3%3A-Generating-Longer-Stories-With-Recursive-and-Yang-Peng/2aab6ca1a8dae3f3db6d248231ac3fa4e222b30a)
2. DOC: Improving Long Story Coherence With Detailed Outline Control (https://www.semanticscholar.org/paper/DOC%3A-Improving-Long-Story-Coherence-With-Detailed-Yang-Klein/ef6c768f23f86c4aa59f7e859ca6ffc1392966ca)

139 - Coherent Long Story Generation, with Kevin Yang

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?

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

ACL 2017 paper by Yonatan Belinkov and others at MIT and QCRI.

Yonatan comes on to tell us about their work.  They trained a neural MT system, then learned models on top of the NMT representation layers to do morphology tasks, trying to probe how much morphological information is encoded by the MT system.  We talk about the specifics of their model and experiments, insights they got from doing these experiments, and how this work relates to other work on representation learning in NLP.

https://www.semanticscholar.org/paper/What-do-Neural-Machine-Translation-Models-Learn-ab-Belinkov-Durrani/37ac87ccea1cc9c78a0921693dd3321246e5ef07

27 - What do Neural Machine Translation Models Learn about Morphology?, with Yonatan Belinkov

NIPS 2017 paper.

We dig into the details of the Transformer, from the "attention is all you need" paper.  Ashish and Jakob give us some motivation for replacing RNNs and CNNs with a more parallelizable self-attention mechanism, they describe how this mechanism works, and then we spend the bulk of the episode trying to get their intuitions for _why_ it works.  We discuss the positional encoding mechanism, multi-headed attention, trying to use these ideas to replace encoders in other models, and what the self-attention actually learns.  Turns out that the lower layers learn something like n-grams (similar to CNNs), and the higher layers learn more semantic-y things, like coreference.

https://www.semanticscholar.org/paper/Attention-Is-All-You-Need-Vaswani-Shazeer/0737da0767d77606169cbf4187b83e1ab62f6077

Minor correction: Talking about complexity equations without the paper in front of you can be tricky, and Ashish and Jakob may have gotten some of the details slightly wrong when we were discussing computational complexity.  The high-level point is that self-attention is cheaper than RNNs when the hidden dimension is higher than the sequence length.  See the paper for more details.

36 - Attention Is All You Need, with Ashish Vaswani and Jakob Uszkoreit

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 writing a peer review starts with a dispassionate summary of what a paper has to offer, followed by the strongest reasons the paper may be accepted, followed by the strongest reasons it may be rejected, and concludes with a list of minor, easy-to-fix problems (e.g., typos) which can be easily addressed in the camera ready. Noah stresses on the importance of thinking about how the reviews we write could demoralize (junior) researchers, and how to be precise and detailed when discussing the weaknesses of a paper to help the authors see the path forward. Other questions we discuss in this episode include: How to read a paper for reviewing purposes? How long it takes to review a paper and how many papers to review? What types of mistakes to be on the lookout for while reviewing? How to review pre-published work?

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

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 Alistair Johnson to discuss the main challenge in applying NLP/ML to clinical domains: the lack of data. We discuss privacy concerns, de-identification, synthesizing records, legal liabilities and data heterogeneity. We also discuss how the MIMIC dataset evolved over the years, how it is being used, and some of the under-explored ways in which it can be used.

Alistair’s homepage: http://alistairewj.github.io/
MIMIC dataset: https://mimic.physionet.org/

93 - NLP/ML for clinical data, with Alistair Johnson

In this episode, we invite Yejin Choi to talk about common sense  knowledge and reasoning, a growing area in NLP. We start by discussing a working definition of “common sense” and the practical utility of studying it. We then talk about some of the datasets and resources focused on studying different aspects of common sense (e.g., ReCoRD, CommonsenseQA, ATOMIC) and contrast implicit vs. explicit modeling of common sense, and what it means for downstream applications. To conclude, Yejin shares her thoughts on some of the open problems in this area and where it is headed in the future.

Yejin Choi’s homepage: https://homes.cs.washington.edu/~yejin/ 
ATOMIC: https://homes.cs.washington.edu/~msap/atomic/ 
ReCoRD: https://sheng-z.github.io/ReCoRD-explorer/ 
CommonsenseQA: https://www.tau-nlp.org/commonsenseqa

95 - Common sense reasoning, with Yejin Choi

In this episode, we talked to Emily Bender about the ethical considerations in developing NLP models and putting them in production. Emily cited specific examples of ethical issues, and talked about the kinds of potential concerns to keep in mind, both when releasing NLP models that will be used by real people, and also while conducting NLP research. We concluded by discussing a set of open-ended questions about designing tasks, collecting data, and publishing results, that Emily has put together towards addressing these concerns.

Emily M. Bender is a Professor in the Department of Linguistics and an Adjunct Professor in the Department of Computer Science and Engineering at the University of Washington. She’s active on Twitter at @emilymbender.

106 - Ethical Considerations In NLP Research, with Emily Bender

In this episode, Tom Kwiatkowski and Michael Collins talk about Natural Questions, a benchmark for question answering research. We discuss how the dataset was collected to reflect naturally-occurring questions, the criteria used for identifying short and long answers, how this dataset differs from other QA datasets, and how easy it might be to game the benchmark with superficial processing of the text. We also contrast the holistic design in Natural Questions to deliberately targeting specific linguistic phenomena of interest when building a QA dataset.

Dataset: https://ai.google.com/research/NaturalQuestions
Paper: https://www.mitpressjournals.org/doi/full/10.1162/tacl_a_00276

110 - Natural Questions, with Tom Kwiatkowski and Michael Collins

We invited Jon Clark from Google to talk about TyDi QA, a new question answering dataset, for this episode. The dataset contains information seeking questions in 11 languages that are typologically diverse, i.e., they differ from each other in terms of key structural and functional features. The questions in TyDiQA are information-seeking, like those in Natural Questions, which we discussed in the previous episode. In addition, TyDiQA also has questions collected in multiple languages using independent crowdsourcing pipelines, as opposed to some other multilingual QA datasets like XQuAD and MLQA where English data is translated into other languages. The dataset and the leaderboard can be accessed at https://ai.google.com/research/tydiqa.

111 - Typologically diverse, multi-lingual, information-seeking questions, with Jon Clark

We invited Marco Tulio Ribeiro, a Senior Researcher at Microsoft, to talk about evaluating NLP models using behavioral testing, a framework borrowed from Software Engineering. Marco describes three kinds of black-box tests the check whether NLP models satisfy certain necessary conditions. While breaking the standard IID assumption, this framework presents a way to evaluate whether NLP systems are ready for real-world use. We also discuss what capabilities can be tested using this framework, how one can come up with good tests, and the need for an evolving set of behavioral tests for NLP systems.

Marco’s homepage: https://homes.cs.washington.edu/~marcotcr/

114 - Behavioral Testing of NLP Models, with Marco Tulio Ribeiro

We interviewed Sameer Singh for this episode, and discussed an overview of recent work in interpreting NLP model predictions, particularly instance-level interpretations. We started out by talking about why it is important to interpret model outputs and why it is a hard problem. We then dove into the details of three kinds of interpretation techniques: attribution based methods, interpretation using influence functions, and generating explanations. Towards the end, we spent some time discussing how explanations of model behavior can be evaluated, and some limitations and potential concerns in evaluation methods.

Sameer Singh is an Assistant Professor of Computer Science at the University of California, Irvine.
Some of the techniques discussed in this episode have been implemented in the AllenNLP Interpret framework (details and demo here: https://allennlp.org/interpret).

117 - Interpreting NLP Model Predictions, with Sameer Singh

We invited Nils Holzenberger, a PhD student at JHU to talk about a dataset involving statutory reasoning in tax law Holzenberger et al. released recently. This dataset includes difficult textual entailment and question answering problems that involve reasoning about how sections in tax law are applicable to specific cases. They also released a Prolog solver that fully solves the problems, and show that learned models using dense representations of text perform poorly. We discussed why this is the case, and how one can train models to solve these challenges.

Project webpage: https://nlp.jhu.edu/law/

122 - Statutory Reasoning in Tax Law, with Nils Holzenberger

**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.

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