Text-to-Text Multi-view Learning for Passage Re-ranking

• URL: http://arxiv.org/abs/2104.14133v1
• Date: Thu, 29 Apr 2021 06:12:34 GMT
• Title: Text-to-Text Multi-view Learning for Passage Re-ranking
• Authors: Jia-Huei Ju, Jheng-Hong Yang, Chuan-Ju Wang
• Abstract summary: We propose a text-to-text multi-view learning framework by incorporating an additional view -- the text generation view -- into a typical single-view passage ranking model. Empirically, the proposed approach is of help to the ranking performance compared to its single-view counterpart.
• Score: 6.3747034690874305
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, much progress in natural language processing has been driven by deep contextualized representations pretrained on large corpora. Typically, the fine-tuning on these pretrained models for a specific downstream task is based on single-view learning, which is however inadequate as a sentence can be interpreted differently from different perspectives. Therefore, in this work, we propose a text-to-text multi-view learning framework by incorporating an additional view -- the text generation view -- into a typical single-view passage ranking model. Empirically, the proposed approach is of help to the ranking performance compared to its single-view counterpart. Ablation studies are also reported in the paper.

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