later

In this episode, Robin Jia talks about how to build robust NLP systems. We discuss the different senses in which a system can be robust, reasons to care about system robustness, and the challenges involved in evaluating robustness of NLP models. We talk about how to build certifiably robust models through interval bound propagation and discrete encoding functions, as well as how to modify data collection procedures through active learning for more robust model development.

Robin Jia is currently a visiting researcher at Facebook AI Research, and will be an assistant professor in the Department of Computer Science at the University of Southern California starting Fall 2021.

123 - Robust NLP, with Robin Jia

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

How can we build Visual Question Answering systems for real users? For this episode, we chatted with Danna Gurari, about her work in building datasets and models towards VQA for people who are blind. We talked about the differences between the existing datasets, and Vizwiz, a dataset built by Gurari et al., and the resulting algorithmic changes. We also discussed the unsolved challenges in this field, and the new tasks they result in.

Danna Gurari is an Assistant Professor as well as Founding Director of the Image and Video Computing group in the School of Information at University of Texas at Austin (UT-Austin).

Vizwiz project page: https://vizwiz.org/

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

125 - VQA for Real Users, with Danna Gurari

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

In this episode, we talk to Shunyu Yao about recent insights into how transformers can represent hierarchical structure in language. Bounded-depth hierarchical structure is thought to be a key feature of natural languages, motivating Shunyu and his coauthors to show that transformers can efficiently represent bounded-depth Dyck languages, which can be thought of as a formal model of the structure of natural languages. We went on to discuss some of the intuitive ideas that emerge from the proofs, connections to RNNs, and insights about positional encodings that may have practical implications. More broadly, we also touched on the role of formal languages and other theoretical tools in modern NLP.

Papers discussed in this episode:

- Self-Attention Networks Can Process Bounded Hierarchical Languages (https://arxiv.org/abs/2105.11115)
- Theoretical Limitations of Self-Attention in Neural Sequence Models (https://arxiv.org/abs/1906.06755)
- RNNs can generate bounded hierarchical languages with optimal memory (https://arxiv.org/abs/2010.07515)
- On the Practical Computational Power of Finite Precision RNNs for Language Recognition (https://arxiv.org/abs/1805.04908)

Shunyu Yao's webpage: https://ysymyth.github.io/

The hosts for this episode are William Merrill and Matt Gardner.

129 - Transformers and Hierarchical Structure, with Shunyu Yao

In this episode, we talked about Coreference Resolution with Marta Recasens, a Research Scientist at Google. We discussed the complexity involved in resolving references in language, the simplification of the problem that the NLP community has focused on by talking about specific datasets, and the complex coreference phenomena that are not yet captured in those datasets. We also briefly talked about how coreference is handled in languages other than English, and how some of the notions we have about modeling coreference phenomena in English do not necessarily transfer to other languages. We ended the discussion by talking about large language models, and to what extent they might be good at handling coreference.

118 - Coreference Resolution, with Marta Recasens

**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.

118 - Coreference Resolution, with Marta Recasens

NLP Highlights