72 - The Anatomy Question Answering Task, with Jordan Boyd-Graber

72 - The Anatomy Question Answering Task, with Jordan Boyd-Graber

Author: Allen Institute for Artificial Intelligence October 17, 2018 Duration: 43:14
Our first episode in a new format: broader surveys of areas, instead of specific discussions on individual papers. In this episode, we talk with Jordan Boyd-Graber about question answering. Matt starts the discussion by giving five different axes on which question answering tasks vary: (1)how complex is the language in the question, (2)what is the genre of the question / nature of the question semantics, (3)what is the context or knowledge source used to answer the question, (4)how much "reasoning" is required to answer the question, and (5) what's the format of the answer? We talk about each of these in detail, giving examples from Jordan's and others' work. In the end, we conclude that "question answering" is a format to study a particular phenomenon, it is not a "phenomenon" in itself. Sometimes it's useful to pose a phenomenon you want to study as a question answering task, and sometimes it's not. During the conversation, Jordan mentioned the QANTA competition; you can find that here: http://qanta.org. We also talked about an adversarial question creation task for Quiz Bowl questions; the paper on that can be found here: https://www.semanticscholar.org/paper/Trick-Me-If-You-Can%3A-Adversarial-Writing-of-Trivia-Wallace-Boyd-Graber/11caf090fef96605d6d67c7505572b1a26796971.

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
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