@inproceedings{fan-etal-2021-flexible,
title = "A Flexible and Extensible Framework for Multiple Answer Modes Question Answering",
author = "Fan, Cheng-Chung and
Kuo, Chia-Chih and
Luo, Shang-Bao and
Liao, Pei-Jun and
Chang, Kuang-Yu and
Hsu, Chiao-Wei and
Wu, Meng-Tse and
Tsai, Shih-Hong and
Wu, Tzu-Man and
Smolka, Aleksandra and
Liang, Chao-Chun and
Wang, Hsin-Min and
Chen, Kuan-Yu and
Tsao, Yu and
Su, Keh-Yih",
editor = "Lee, Lung-Hao and
Chang, Chia-Hui and
Chen, Kuan-Yu",
booktitle = "Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)",
month = oct,
year = "2021",
address = "Taoyuan, Taiwan",
publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
url = "https://aclanthology.org/2021.rocling-1.5/",
pages = "33--42",
abstract = "This paper presents a framework to answer the questions that require various kinds of inference mechanisms (such as Extraction, Entailment-Judgement, and Summarization). Most of the previous approaches adopt a rigid framework which handles only one inference mechanism. Only a few of them adopt several answer generation modules for providing different mechanisms; however, they either lack an aggregation mechanism to merge the answers from various modules, or are too complicated to be implemented with neural networks. To alleviate the problems mentioned above, we propose a divide-and-conquer framework, which consists of a set of various answer generation modules, a dispatch module, and an aggregation module. The answer generation modules are designed to provide different inference mechanisms, the dispatch module is used to select a few appropriate answer generation modules to generate answer candidates, and the aggregation module is employed to select the final answer. We test our framework on the 2020 Formosa Grand Challenge Contest dataset. Experiments show that the proposed framework outperforms the state-of-the-art Roberta-large model by about 11.4{\%}."
}
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<abstract>This paper presents a framework to answer the questions that require various kinds of inference mechanisms (such as Extraction, Entailment-Judgement, and Summarization). Most of the previous approaches adopt a rigid framework which handles only one inference mechanism. Only a few of them adopt several answer generation modules for providing different mechanisms; however, they either lack an aggregation mechanism to merge the answers from various modules, or are too complicated to be implemented with neural networks. To alleviate the problems mentioned above, we propose a divide-and-conquer framework, which consists of a set of various answer generation modules, a dispatch module, and an aggregation module. The answer generation modules are designed to provide different inference mechanisms, the dispatch module is used to select a few appropriate answer generation modules to generate answer candidates, and the aggregation module is employed to select the final answer. We test our framework on the 2020 Formosa Grand Challenge Contest dataset. Experiments show that the proposed framework outperforms the state-of-the-art Roberta-large model by about 11.4%.</abstract>
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%0 Conference Proceedings
%T A Flexible and Extensible Framework for Multiple Answer Modes Question Answering
%A Fan, Cheng-Chung
%A Kuo, Chia-Chih
%A Luo, Shang-Bao
%A Liao, Pei-Jun
%A Chang, Kuang-Yu
%A Hsu, Chiao-Wei
%A Wu, Meng-Tse
%A Tsai, Shih-Hong
%A Wu, Tzu-Man
%A Smolka, Aleksandra
%A Liang, Chao-Chun
%A Wang, Hsin-Min
%A Chen, Kuan-Yu
%A Tsao, Yu
%A Su, Keh-Yih
%Y Lee, Lung-Hao
%Y Chang, Chia-Hui
%Y Chen, Kuan-Yu
%S Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)
%D 2021
%8 October
%I The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
%C Taoyuan, Taiwan
%F fan-etal-2021-flexible
%X This paper presents a framework to answer the questions that require various kinds of inference mechanisms (such as Extraction, Entailment-Judgement, and Summarization). Most of the previous approaches adopt a rigid framework which handles only one inference mechanism. Only a few of them adopt several answer generation modules for providing different mechanisms; however, they either lack an aggregation mechanism to merge the answers from various modules, or are too complicated to be implemented with neural networks. To alleviate the problems mentioned above, we propose a divide-and-conquer framework, which consists of a set of various answer generation modules, a dispatch module, and an aggregation module. The answer generation modules are designed to provide different inference mechanisms, the dispatch module is used to select a few appropriate answer generation modules to generate answer candidates, and the aggregation module is employed to select the final answer. We test our framework on the 2020 Formosa Grand Challenge Contest dataset. Experiments show that the proposed framework outperforms the state-of-the-art Roberta-large model by about 11.4%.
%U https://aclanthology.org/2021.rocling-1.5/
%P 33-42
Markdown (Informal)
[A Flexible and Extensible Framework for Multiple Answer Modes Question Answering](https://aclanthology.org/2021.rocling-1.5/) (Fan et al., ROCLING 2021)
ACL
- Cheng-Chung Fan, Chia-Chih Kuo, Shang-Bao Luo, Pei-Jun Liao, Kuang-Yu Chang, Chiao-Wei Hsu, Meng-Tse Wu, Shih-Hong Tsai, Tzu-Man Wu, Aleksandra Smolka, Chao-Chun Liang, Hsin-Min Wang, Kuan-Yu Chen, Yu Tsao, and Keh-Yih Su. 2021. A Flexible and Extensible Framework for Multiple Answer Modes Question Answering. In Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021), pages 33–42, Taoyuan, Taiwan. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP).