Au sujet de ce document
- sl:arxiv_author :
- sl:arxiv_firstAuthor : Zhenrui Yue
- sl:arxiv_num : 2108.13854
- sl:arxiv_published : 2021-08-31T14:05:55Z
- sl:arxiv_summary : Question generation has recently shown impressive results in customizing
question answering (QA) systems to new domains. These approaches circumvent the
need for manually annotated training data from the new domain and, instead,
generate synthetic question-answer pairs that are used for training. However,
existing methods for question generation rely on large amounts of synthetically
generated datasets and costly computational resources, which render these
techniques widely inaccessible when the text corpora is of limited size. This
is problematic as many niche domains rely on small text corpora, which
naturally restricts the amount of synthetic data that can be generated. In this
paper, we propose a novel framework for domain adaptation called contrastive
domain adaptation for QA (CAQA). Specifically, CAQA combines techniques from
question generation and domain-invariant learning to answer out-of-domain
questions in settings with limited text corpora. Here, we train a QA system on
both source data and generated data from the target domain with a contrastive
adaptation loss that is incorporated in the training objective. By combining
techniques from question generation and domain-invariant learning, our model
achieved considerable improvements compared to state-of-the-art baselines.@en
- sl:arxiv_title : Contrastive Domain Adaptation for Question Answering using Limited Text Corpora@en
- sl:arxiv_updated : 2021-08-31T14:05:55Z
- sl:bookmarkOf : https://arxiv.org/abs/2108.13854
- sl:creationDate : 2021-11-19
- sl:creationTime : 2021-11-19T00:18:40Z
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