@inproceedings{kacupaj-etal-2021-conversational,
title = "Conversational Question Answering over Knowledge Graphs with Transformer and Graph Attention Networks",
author = "Kacupaj, Endri and
Plepi, Joan and
Singh, Kuldeep and
Thakkar, Harsh and
Lehmann, Jens and
Maleshkova, Maria",
editor = "Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.72",
doi = "10.18653/v1/2021.eacl-main.72",
pages = "850--862",
abstract = "This paper addresses the task of (complex) conversational question answering over a knowledge graph. For this task, we propose LASAGNE (muLti-task semAntic parSing with trAnsformer and Graph atteNtion nEworks). It is the first approach, which employs a transformer architecture extended with Graph Attention Networks for multi-task neural semantic parsing. LASAGNE uses a transformer model for generating the base logical forms, while the Graph Attention model is used to exploit correlations between (entity) types and predicates to produce node representations. LASAGNE also includes a novel entity recognition module which detects, links, and ranks all relevant entities in the question context. We evaluate LASAGNE on a standard dataset for complex sequential question answering, on which it outperforms existing baselines averaged on all question types. Specifically, we show that LASAGNE improves the F1-score on eight out of ten question types; in some cases, the increase is more than 20{\%} compared to state of the art (SotA).",
}
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%0 Conference Proceedings
%T Conversational Question Answering over Knowledge Graphs with Transformer and Graph Attention Networks
%A Kacupaj, Endri
%A Plepi, Joan
%A Singh, Kuldeep
%A Thakkar, Harsh
%A Lehmann, Jens
%A Maleshkova, Maria
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F kacupaj-etal-2021-conversational
%X This paper addresses the task of (complex) conversational question answering over a knowledge graph. For this task, we propose LASAGNE (muLti-task semAntic parSing with trAnsformer and Graph atteNtion nEworks). It is the first approach, which employs a transformer architecture extended with Graph Attention Networks for multi-task neural semantic parsing. LASAGNE uses a transformer model for generating the base logical forms, while the Graph Attention model is used to exploit correlations between (entity) types and predicates to produce node representations. LASAGNE also includes a novel entity recognition module which detects, links, and ranks all relevant entities in the question context. We evaluate LASAGNE on a standard dataset for complex sequential question answering, on which it outperforms existing baselines averaged on all question types. Specifically, we show that LASAGNE improves the F1-score on eight out of ten question types; in some cases, the increase is more than 20% compared to state of the art (SotA).
%R 10.18653/v1/2021.eacl-main.72
%U https://aclanthology.org/2021.eacl-main.72
%U https://doi.org/10.18653/v1/2021.eacl-main.72
%P 850-862
Markdown (Informal)
[Conversational Question Answering over Knowledge Graphs with Transformer and Graph Attention Networks](https://aclanthology.org/2021.eacl-main.72) (Kacupaj et al., EACL 2021)
ACL