@inproceedings{mueller-etal-2019-answering,
title = "Answering Conversational Questions on Structured Data without Logical Forms",
author = "Mueller, Thomas and
Piccinno, Francesco and
Shaw, Peter and
Nicosia, Massimo and
Altun, Yasemin",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1603",
doi = "10.18653/v1/D19-1603",
pages = "5902--5910",
abstract = "We present a novel approach to answering sequential questions based on structured objects such as knowledge bases or tables without using a logical form as an intermediate representation. We encode tables as graphs using a graph neural network model based on the Transformer architecture. The answers are then selected from the encoded graph using a pointer network. This model is appropriate for processing conversations around structured data, where the attention mechanism that selects the answers to a question can also be used to resolve conversational references. We demonstrate the validity of this approach with competitive results on the Sequential Question Answering (SQA) task.",
}
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%0 Conference Proceedings
%T Answering Conversational Questions on Structured Data without Logical Forms
%A Mueller, Thomas
%A Piccinno, Francesco
%A Shaw, Peter
%A Nicosia, Massimo
%A Altun, Yasemin
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F mueller-etal-2019-answering
%X We present a novel approach to answering sequential questions based on structured objects such as knowledge bases or tables without using a logical form as an intermediate representation. We encode tables as graphs using a graph neural network model based on the Transformer architecture. The answers are then selected from the encoded graph using a pointer network. This model is appropriate for processing conversations around structured data, where the attention mechanism that selects the answers to a question can also be used to resolve conversational references. We demonstrate the validity of this approach with competitive results on the Sequential Question Answering (SQA) task.
%R 10.18653/v1/D19-1603
%U https://aclanthology.org/D19-1603
%U https://doi.org/10.18653/v1/D19-1603
%P 5902-5910
Markdown (Informal)
[Answering Conversational Questions on Structured Data without Logical Forms](https://aclanthology.org/D19-1603) (Mueller et al., EMNLP-IJCNLP 2019)
ACL
- Thomas Mueller, Francesco Piccinno, Peter Shaw, Massimo Nicosia, and Yasemin Altun. 2019. Answering Conversational Questions on Structured Data without Logical Forms. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5902–5910, Hong Kong, China. Association for Computational Linguistics.