@inproceedings{du-etal-2020-leveraging,
title = "Leveraging Structured Metadata for Improving Question Answering on the Web",
author = "Du, Xinya and
Awadallah, Ahmed Hassan and
Fourney, Adam and
Sim, Robert and
Bennett, Paul and
Cardie, Claire",
editor = "Wong, Kam-Fai and
Knight, Kevin and
Wu, Hua",
booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.aacl-main.55",
doi = "10.18653/v1/2020.aacl-main.55",
pages = "551--556",
abstract = "We show that leveraging metadata information from web pages can improve the performance of models for answer passage selection/reranking. We propose a neural passage selection model that leverages metadata information with a fine-grained encoding strategy, which learns the representation for metadata predicates in a hierarchical way. The models are evaluated on the MS MARCO (Nguyen et al., 2016) and Recipe-MARCO datasets. Results show that our models significantly outperform baseline models, which do not incorporate metadata. We also show that the fine-grained encoding{'}s advantage over other strategies for encoding the metadata.",
}
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<abstract>We show that leveraging metadata information from web pages can improve the performance of models for answer passage selection/reranking. We propose a neural passage selection model that leverages metadata information with a fine-grained encoding strategy, which learns the representation for metadata predicates in a hierarchical way. The models are evaluated on the MS MARCO (Nguyen et al., 2016) and Recipe-MARCO datasets. Results show that our models significantly outperform baseline models, which do not incorporate metadata. We also show that the fine-grained encoding’s advantage over other strategies for encoding the metadata.</abstract>
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%0 Conference Proceedings
%T Leveraging Structured Metadata for Improving Question Answering on the Web
%A Du, Xinya
%A Awadallah, Ahmed Hassan
%A Fourney, Adam
%A Sim, Robert
%A Bennett, Paul
%A Cardie, Claire
%Y Wong, Kam-Fai
%Y Knight, Kevin
%Y Wu, Hua
%S Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing
%D 2020
%8 December
%I Association for Computational Linguistics
%C Suzhou, China
%F du-etal-2020-leveraging
%X We show that leveraging metadata information from web pages can improve the performance of models for answer passage selection/reranking. We propose a neural passage selection model that leverages metadata information with a fine-grained encoding strategy, which learns the representation for metadata predicates in a hierarchical way. The models are evaluated on the MS MARCO (Nguyen et al., 2016) and Recipe-MARCO datasets. Results show that our models significantly outperform baseline models, which do not incorporate metadata. We also show that the fine-grained encoding’s advantage over other strategies for encoding the metadata.
%R 10.18653/v1/2020.aacl-main.55
%U https://aclanthology.org/2020.aacl-main.55
%U https://doi.org/10.18653/v1/2020.aacl-main.55
%P 551-556
Markdown (Informal)
[Leveraging Structured Metadata for Improving Question Answering on the Web](https://aclanthology.org/2020.aacl-main.55) (Du et al., AACL 2020)
ACL
- Xinya Du, Ahmed Hassan Awadallah, Adam Fourney, Robert Sim, Paul Bennett, and Claire Cardie. 2020. Leveraging Structured Metadata for Improving Question Answering on the Web. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, pages 551–556, Suzhou, China. Association for Computational Linguistics.