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arXiv · 2106.08190

Question Answering Infused Pre-training of General-Purpose Contextualized Representations

Robin Jia·Mike Lewis·Luke Zettlemoyer
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Abstract

We propose a pre-training objective based on question answering (QA) for learning general-purpose contextual representations, motivated by the intuition that the representation of a phrase in a passage should encode all questions that the phrase can answer in context. To this end, we train a bi-encoder QA model, which independently encodes passages and questions, to match the predictions of a more accurate cross-encoder model on 80 million synthesized QA pairs. By encoding QA-relevant information, the bi-encoder's token-level representations are useful for non-QA downstream tasks without extensive (or in some cases, any) fine-tuning. We show large improvements over both RoBERTa-large and previous state-of-the-art results on zero-shot and few-shot paraphrase detection on four datasets, few-shot named entity recognition on two datasets, and zero-shot sentiment analysis on three datasets.

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Robin Jia, Mike Lewis, Luke Zettlemoyer. 2021-06-15. Question Answering Infused Pre-training of General-Purpose Contextualized Representations. https://arxiv.org/abs/2106.08190

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