118 - Coreference Resolution, with Marta Recasens

118 - Coreference Resolution, with Marta Recasens

Author: Allen Institute for Artificial Intelligence August 27, 2020 Duration: 47:30
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.

While NLP Highlights is currently on hiatus, its archive remains a compelling snapshot of conversations from the front lines of computational linguistics. Produced by the Allen Institute for Artificial Intelligence, this science podcast carved out a space for deep, researcher-led discussions about natural language processing. Each episode functions as an informal seminar, where the people actively designing algorithms and pushing the field forward explain their work in their own words. You'll hear about the nuanced challenges behind making machines understand, generate, and reason with human language, from foundational theories to unexpected applications. The dialogue in this podcast often delves into the "why" behind the research, not just the results, offering clarity on complex topics like machine translation, sentiment analysis, or large language models. It’s a chance to listen as experts articulate their thought processes, debates, and moments of insight, with all the candidness that comes from a conversation between peers. The views shared are personal perspectives from the hosts and their guests, independent of their affiliated institutions. For anyone curious about how machines learn to parse meaning, the archived episodes of NLP Highlights provide a thoughtful and accessible entry point.
Author: Language: English Episodes: 100

NLP Highlights
Podcast Episodes
83 - Knowledge Base Construction, with Sebastian Riedel [not-audio_url] [/not-audio_url]

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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…
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80 - Leaderboards and Science, with Siva Reddy [not-audio_url] [/not-audio_url]

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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…
79 - The glass ceiling in NLP, with Natalie Schluter [not-audio_url] [/not-audio_url]

Duration: 26:31
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Duration: 30:21
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77. On Writing Quality Peer Reviews, with Noah A. Smith [not-audio_url] [/not-audio_url]

Duration: 38:12
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75 - Reinforcement / Imitation Learning in NLP, with Hal Daumé III [not-audio_url] [/not-audio_url]

Duration: 43:54
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 cir…
74 - Deep Reinforcement Learning Doesn't Work Yet, with Alex Irpan [not-audio_url] [/not-audio_url]

Duration: 40:43
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 m…