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Keyword: BERT pre-training ×Clear all

2 claims from 1 paper

1. Computer Science › Topic Modeling

   SpanBERT: Improving Pre-training by Representing and Predicting Spans

   Joshi, Chen, Liu, Weld, Zettlemoyer and Levy · Transactions of the Association for Computational Linguistics · 2020

   The paper presents SpanBERT, which extends BERT by masking contiguous spans and predicting them from boundary representations, and reports gains on question answering, coreference, relation extraction and GLUE.

   Unchecked2 claims
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   1. UncheckedSpanBERT, a pre-training method built around text spans, beat BERT and tuned baselines, with big gains on question answering and coreference tasks.“SpanBERT consistently outperforms BERT and our better-tuned baselines, with substantial gains on span selection tasks such as question answering and coreference resolution.”
   2. UncheckedWith BERT-large's training data and model size, a single SpanBERT model scores 94.6% F1 on SQuAD 1.1 and 88.7% F1 on SQuAD 2.0.“In particular, with the same training data and model size as BERT-large, our single model obtains 94.6% and 88.7% F1 on SQuAD 1.1 and 2.0, respectively.”