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112 - Alignment of Multilingual Contextual Representations, with Steven Cao

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Manage episode 261766284 series 1452120
内容由NLP Highlights and Allen Institute for Artificial Intelligence提供。所有播客内容(包括剧集、图形和播客描述)均由 NLP Highlights and Allen Institute for Artificial Intelligence 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal
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/
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Manage episode 261766284 series 1452120
内容由NLP Highlights and Allen Institute for Artificial Intelligence提供。所有播客内容(包括剧集、图形和播客描述)均由 NLP Highlights and Allen Institute for Artificial Intelligence 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal
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/
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