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        <title>FastHybrid: A Hybrid Model for Efficient Answer Selection</title>
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    <abstract>Answer selection is a core component in any question-answering systems. It aims to select correct answer sentences for a given question from a pool of candidate sentences. In recent years, many deep learning methods have been proposed and shown excellent results for this task. However, these methods typically require extensive parameter (and hyper-parameter) tuning, which give rise to efficiency issues for large-scale datasets, and potentially make them less portable across new datasets and domains (as re-tuning is usually required). In this paper, we propose an extremely efficient hybrid model (FastHybrid) that tackles the problem from both an accuracy and scalability point of view. FastHybrid is a light-weight model that requires little tuning and adaptation across different domains. It combines a fast deep model (which will be introduced in the method section) with an initial information retrieval model to effectively and efficiently handle answer selection. We introduce a new efficient attention mechanism in the hybrid model and demonstrate its effectiveness on several QA datasets. Experimental results show that although the hybrid uses no training data, its accuracy is often on-par with supervised deep learning techniques, while significantly reducing training and tuning costs across different domains.</abstract>
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%0 Conference Proceedings
%T FastHybrid: A Hybrid Model for Efficient Answer Selection
%A Wang, Lidan
%A Tan, Ming
%A Han, Jiawei
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F wang-etal-2016-fasthybrid
%X Answer selection is a core component in any question-answering systems. It aims to select correct answer sentences for a given question from a pool of candidate sentences. In recent years, many deep learning methods have been proposed and shown excellent results for this task. However, these methods typically require extensive parameter (and hyper-parameter) tuning, which give rise to efficiency issues for large-scale datasets, and potentially make them less portable across new datasets and domains (as re-tuning is usually required). In this paper, we propose an extremely efficient hybrid model (FastHybrid) that tackles the problem from both an accuracy and scalability point of view. FastHybrid is a light-weight model that requires little tuning and adaptation across different domains. It combines a fast deep model (which will be introduced in the method section) with an initial information retrieval model to effectively and efficiently handle answer selection. We introduce a new efficient attention mechanism in the hybrid model and demonstrate its effectiveness on several QA datasets. Experimental results show that although the hybrid uses no training data, its accuracy is often on-par with supervised deep learning techniques, while significantly reducing training and tuning costs across different domains.
%U https://aclanthology.org/C16-1224
%P 2378-2388

Markdown (Informal)

[FastHybrid: A Hybrid Model for Efficient Answer Selection](https://aclanthology.org/C16-1224) (Wang et al., COLING 2016)

• FastHybrid: A Hybrid Model for Efficient Answer Selection (Wang et al., COLING 2016)

ACL

• Lidan Wang, Ming Tan, and Jiawei Han. 2016. FastHybrid: A Hybrid Model for Efficient Answer Selection. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2378–2388, Osaka, Japan. The COLING 2016 Organizing Committee.