Utilizing Bidirectional Encoder Representations from Transformers for Answer Selection

• URL: http://arxiv.org/abs/2011.07208v1
• Date: Sat, 14 Nov 2020 03:15:26 GMT
• Title: Utilizing Bidirectional Encoder Representations from Transformers for Answer Selection
• Authors: Md Tahmid Rahman Laskar, Enamul Hoque, Jimmy Xiangji Huang
• Abstract summary: We adopt a transformer-based model for the language modeling task in a large dataset and fine-tune it for downstream tasks. We find that fine-tuning the BERT model for the answer selection task is very effective and observe a maximum improvement of 13.1% in the QA datasets and 18.7% in the CQA datasets.
• Score: 16.048329028104643
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pre-training a transformer-based model for the language modeling task in a large dataset and then fine-tuning it for downstream tasks has been found very useful in recent years. One major advantage of such pre-trained language models is that they can effectively absorb the context of each word in a sentence. However, for tasks such as the answer selection task, the pre-trained language models have not been extensively used yet. To investigate their effectiveness in such tasks, in this paper, we adopt the pre-trained Bidirectional Encoder Representations from Transformer (BERT) language model and fine-tune it on two Question Answering (QA) datasets and three Community Question Answering (CQA) datasets for the answer selection task. We find that fine-tuning the BERT model for the answer selection task is very effective and observe a maximum improvement of 13.1% in the QA datasets and 18.7% in the CQA datasets compared to the previous state-of-the-art.

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