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        <title>An End-to-End Deep Framework for Answer Triggering with a Novel Group-Level Objective</title>
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    <abstract>Given a question and a set of answer candidates, answer triggering determines whether the candidate set contains any correct answers. If yes, it then outputs a correct one. In contrast to existing pipeline methods which first consider individual candidate answers separately and then make a prediction based on a threshold, we propose an end-to-end deep neural network framework, which is trained by a novel group-level objective function that directly optimizes the answer triggering performance. Our objective function penalizes three potential types of error and allows training the framework in an end-to-end manner. Experimental results on the WikiQA benchmark show that our framework outperforms the state of the arts by a 6.6% absolute gain under F1 measure.</abstract>
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%0 Conference Proceedings
%T An End-to-End Deep Framework for Answer Triggering with a Novel Group-Level Objective
%A Zhao, Jie
%A Su, Yu
%A Guan, Ziyu
%A Sun, Huan
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F zhao-etal-2017-end
%X Given a question and a set of answer candidates, answer triggering determines whether the candidate set contains any correct answers. If yes, it then outputs a correct one. In contrast to existing pipeline methods which first consider individual candidate answers separately and then make a prediction based on a threshold, we propose an end-to-end deep neural network framework, which is trained by a novel group-level objective function that directly optimizes the answer triggering performance. Our objective function penalizes three potential types of error and allows training the framework in an end-to-end manner. Experimental results on the WikiQA benchmark show that our framework outperforms the state of the arts by a 6.6% absolute gain under F1 measure.
%R 10.18653/v1/D17-1131
%U https://aclanthology.org/D17-1131
%U https://doi.org/10.18653/v1/D17-1131
%P 1276-1282

Markdown (Informal)

[An End-to-End Deep Framework for Answer Triggering with a Novel Group-Level Objective](https://aclanthology.org/D17-1131) (Zhao et al., EMNLP 2017)

• An End-to-End Deep Framework for Answer Triggering with a Novel Group-Level Objective (Zhao et al., EMNLP 2017)

ACL

• Jie Zhao, Yu Su, Ziyu Guan, and Huan Sun. 2017. An End-to-End Deep Framework for Answer Triggering with a Novel Group-Level Objective. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1276–1282, Copenhagen, Denmark. Association for Computational Linguistics.