NLP Highlights

NLP Highlights

Welcome to the NLP highlights podcast, where we i…


128 - Dynamic Benchmarking, with Douwe Kiela
47 Minuten
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...
127 - Masakhane and Participatory Research for African Languages, with Tosin Adewumi and Perez Ogayo
47 Minuten
We invited members of Masakhane, Tosin Adewumi and Perez Ogayo, to talk about their EMNLP Findings paper that discusses why typical research is limited for low-resourced NLP and how participatory research can help.   As a result of participatory rese...
126 - Optimizing Continuous Prompts for Generation, with Lisa Li
48 Minuten
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...
125 - VQA for Real Users, with Danna Gurari
42 Minuten
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 existi...
124 - Semantic Machines and Task-Oriented Dialog, with Jayant Krishnamurthy and Hao Fang
46 Minuten
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-orie...

Über diesen Podcast

Welcome to the NLP highlights podcast, where we invite researchers to talk about their work in various areas in natural language processing. The hosts are the members of the AllenNLP team at Allen Institute for AI. All views expressed belong to the hosts and guests and do not represent their employers.

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