112 - Alignment of Multilingual Contextual Representations, with Steven Cao

112 - Alignment of Multilingual Contextual Representations, with Steven Cao

Author: Allen Institute for Artificial Intelligence May 13, 2020 Duration: 33:15
We invited Steven Cao to talk about his paper on multilingual alignment of contextual word embeddings. We started by discussing how multilingual transformers work in general, and then focus on Steven’s work on aligning word representations. The core idea is to start from a list of words automatically aligned from parallel corpora and to ensure the representations of the aligned words are similar to each other while not moving too far away from their original representations. We discussed the experiments on the XNLI dataset in the paper, analysis, and the decision to do the alignment at word level and compare it to other possibilities such as aligning word pieces or higher level encoded representations in transformers. Paper: https://openreview.net/forum?id=r1xCMyBtPS Steven Cao’s webpage: https://stevenxcao.github.io/

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
129 - Transformers and Hierarchical Structure, with Shunyu Yao [not-audio_url] [/not-audio_url]

Duration: 35:43
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 language…
128 - Dynamic Benchmarking, with Douwe Kiela [not-audio_url] [/not-audio_url]

Duration: 47:00
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 Dynaben…
126 - Optimizing Continuous Prompts for Generation, with Lisa Li [not-audio_url] [/not-audio_url]

Duration: 47:38
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-sp…
125 - VQA for Real Users, with Danna Gurari [not-audio_url] [/not-audio_url]

Duration: 42:10
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…