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arXiv · 2010.05904

Multi-Stage Pre-training for Low-Resource Domain Adaptation

Rong Zhang·Revanth Gangi Reddy·Md Arafat Sultan·Vittorio Castelli·Anthony Ferritto·Radu Florian·Efsun Sarioglu Kayi·Salim Roukos·Avirup Sil·Todd Ward
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Abstract

Transfer learning techniques are particularly useful in NLP tasks where a sizable amount of high-quality annotated data is difficult to obtain. Current approaches directly adapt a pre-trained language model (LM) on in-domain text before fine-tuning to downstream tasks. We show that extending the vocabulary of the LM with domain-specific terms leads to further gains. To a bigger effect, we utilize structure in the unlabeled data to create auxiliary synthetic tasks, which helps the LM transfer to downstream tasks. We apply these approaches incrementally on a pre-trained Roberta-large LM and show considerable performance gain on three tasks in the IT domain: Extractive Reading Comprehension, Document Ranking and Duplicate Question Detection.

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Rong Zhang, Revanth Gangi Reddy, Md Arafat Sultan, Vittorio Castelli, Anthony Ferritto, Radu Florian, Efsun Sarioglu Kayi, Salim Roukos, Avirup Sil, Todd Ward. 2020-10-12. Multi-Stage Pre-training for Low-Resource Domain Adaptation. https://arxiv.org/abs/2010.05904

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