MIX : a Multi-task Learning Approach to Solve Open-Domain Question Answering
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arXiv
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| Format: | Preprint |
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2020
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| _version_ | 1866915195544141824 |
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| author | Chaybouti, Sofian Saghe, Achraf Shabou, Aymen |
| author_facet | Chaybouti, Sofian Saghe, Achraf Shabou, Aymen |
| contents | This paper introduces MIX, a multi-task deep learning approach to solve open-ended question-answering. First, we design our system as a multi-stage pipeline of 3 building blocks: a BM25-based Retriever to reduce the search space, a RoBERTa-based Scorer, and an Extractor to rank retrieved paragraphs and extract relevant text spans, respectively. Eventually, we further improve the computational efficiency of our system to deal with the scalability challenge: thanks to multi-task learning, we parallelize the close tasks solved by the Scorer and the Extractor. Our system is on par with state-of-the-art performances on the squad-open benchmark while being simpler conceptually. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2012_09766 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | MIX : a Multi-task Learning Approach to Solve Open-Domain Question Answering Chaybouti, Sofian Saghe, Achraf Shabou, Aymen Computation and Language I.2.7 This paper introduces MIX, a multi-task deep learning approach to solve open-ended question-answering. First, we design our system as a multi-stage pipeline of 3 building blocks: a BM25-based Retriever to reduce the search space, a RoBERTa-based Scorer, and an Extractor to rank retrieved paragraphs and extract relevant text spans, respectively. Eventually, we further improve the computational efficiency of our system to deal with the scalability challenge: thanks to multi-task learning, we parallelize the close tasks solved by the Scorer and the Extractor. Our system is on par with state-of-the-art performances on the squad-open benchmark while being simpler conceptually. |
| title | MIX : a Multi-task Learning Approach to Solve Open-Domain Question Answering |
| topic | Computation and Language I.2.7 |
| url | https://arxiv.org/abs/2012.09766 |