Question Classification with Deep Contextualized Transformer
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arXiv
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| Format: | Preprint |
| Published: |
2019
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| _version_ | 1866909220505387008 |
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| author | Luo, Haozheng Liu, Ningwei Feng, Charles |
| author_facet | Luo, Haozheng Liu, Ningwei Feng, Charles |
| contents | The latest work for Question and Answer problems is to use the Stanford Parse Tree. We build on prior work and develop a new method to handle the Question and Answer problem with the Deep Contextualized Transformer to manage some aberrant expressions. We also conduct extensive evaluations of the SQuAD and SwDA dataset and show significant improvement over QA problem classification of industry needs. We also investigate the impact of different models for the accuracy and efficiency of the problem answers. It shows that our new method is more effective for solving QA problems with higher accuracy |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1910_10492 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Question Classification with Deep Contextualized Transformer Luo, Haozheng Liu, Ningwei Feng, Charles Computation and Language Artificial Intelligence The latest work for Question and Answer problems is to use the Stanford Parse Tree. We build on prior work and develop a new method to handle the Question and Answer problem with the Deep Contextualized Transformer to manage some aberrant expressions. We also conduct extensive evaluations of the SQuAD and SwDA dataset and show significant improvement over QA problem classification of industry needs. We also investigate the impact of different models for the accuracy and efficiency of the problem answers. It shows that our new method is more effective for solving QA problems with higher accuracy |
| title | Question Classification with Deep Contextualized Transformer |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/1910.10492 |