@inproceedings{srivastava-etal-2021-complex,
title = "Complex Question Answering on knowledge graphs using machine translation and multi-task learning",
author = "Srivastava, Saurabh and
Patidar, Mayur and
Chowdhury, Sudip and
Agarwal, Puneet and
Bhattacharya, Indrajit and
Shroff, Gautam",
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.300",
doi = "10.18653/v1/2021.eacl-main.300",
pages = "3428--3439",
abstract = "Question answering (QA) over a knowledge graph (KG) is a task of answering a natural language (NL) query using the information stored in KG. In a real-world industrial setting, this involves addressing multiple challenges including entity linking, multi-hop reasoning over KG, etc. Traditional approaches handle these challenges in a modularized sequential manner where errors in one module lead to the accumulation of errors in downstream modules. Often these challenges are inter-related and the solutions to them can reinforce each other when handled simultaneously in an end-to-end learning setup. To this end, we propose a multi-task BERT based Neural Machine Translation (NMT) model to address these challenges. Through experimental analysis, we demonstrate the efficacy of our proposed approach on one publicly available and one proprietary dataset.",
}
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%0 Conference Proceedings
%T Complex Question Answering on knowledge graphs using machine translation and multi-task learning
%A Srivastava, Saurabh
%A Patidar, Mayur
%A Chowdhury, Sudip
%A Agarwal, Puneet
%A Bhattacharya, Indrajit
%A Shroff, Gautam
%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 srivastava-etal-2021-complex
%X Question answering (QA) over a knowledge graph (KG) is a task of answering a natural language (NL) query using the information stored in KG. In a real-world industrial setting, this involves addressing multiple challenges including entity linking, multi-hop reasoning over KG, etc. Traditional approaches handle these challenges in a modularized sequential manner where errors in one module lead to the accumulation of errors in downstream modules. Often these challenges are inter-related and the solutions to them can reinforce each other when handled simultaneously in an end-to-end learning setup. To this end, we propose a multi-task BERT based Neural Machine Translation (NMT) model to address these challenges. Through experimental analysis, we demonstrate the efficacy of our proposed approach on one publicly available and one proprietary dataset.
%R 10.18653/v1/2021.eacl-main.300
%U https://aclanthology.org/2021.eacl-main.300
%U https://doi.org/10.18653/v1/2021.eacl-main.300
%P 3428-3439
Markdown (Informal)
[Complex Question Answering on knowledge graphs using machine translation and multi-task learning](https://aclanthology.org/2021.eacl-main.300) (Srivastava et al., EACL 2021)
ACL