@inproceedings{thirukovalluru-etal-2021-knowledge,
title = "Knowledge Informed Semantic Parsing for Conversational Question Answering",
author = "Thirukovalluru, Raghuveer and
Sridhar, Mukund and
Thai, Dung and
Chanumolu, Shruti and
Monath, Nicholas and
Ananthakrishnan, Sankaranarayanan and
McCallum, Andrew",
editor = "Rogers, Anna and
Calixto, Iacer and
Vuli{\'c}, Ivan and
Saphra, Naomi and
Kassner, Nora and
Camburu, Oana-Maria and
Bansal, Trapit and
Shwartz, Vered",
booktitle = "Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.repl4nlp-1.24",
doi = "10.18653/v1/2021.repl4nlp-1.24",
pages = "231--240",
abstract = "Smart assistants are tasked to answer various questions regarding world knowledge. These questions range from retrieval of simple facts to retrieval of complex, multi-hops question followed by various operators (i.e., filter, argmax). Semantic parsing has emerged as the state-of-the-art for answering these kinds of questions by forming queries to extract information from knowledge bases (KBs). Specially, neural semantic parsers (NSPs) effectively translate natural questions to logical forms, which execute on KB and give desirable answers. Yet, NSPs suffer from non-executable logical forms for some instances in the generated logical forms might be missing due to the incompleteness of KBs. Intuitively, knowing the KB structure informs NSP with changes of the global logical forms structures with respect to changes in KB instances. In this work, we propose a novel knowledge-informed decoder variant of NSP. We consider the conversational question answering settings, where a natural language query, its context and its final answers are available at training. Experimental results show that our method outperformed strong baselines by 1.8 F1 points overall across 10 types of questions of the CSQA dataset. Especially for the {``}Logical Reasoning{''} category, our model improves by 7 F1 points. Furthermore, our results are achieved with 90.3{\%} fewer parameters, allowing faster training for large-scale datasets.",
}
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<abstract>Smart assistants are tasked to answer various questions regarding world knowledge. These questions range from retrieval of simple facts to retrieval of complex, multi-hops question followed by various operators (i.e., filter, argmax). Semantic parsing has emerged as the state-of-the-art for answering these kinds of questions by forming queries to extract information from knowledge bases (KBs). Specially, neural semantic parsers (NSPs) effectively translate natural questions to logical forms, which execute on KB and give desirable answers. Yet, NSPs suffer from non-executable logical forms for some instances in the generated logical forms might be missing due to the incompleteness of KBs. Intuitively, knowing the KB structure informs NSP with changes of the global logical forms structures with respect to changes in KB instances. In this work, we propose a novel knowledge-informed decoder variant of NSP. We consider the conversational question answering settings, where a natural language query, its context and its final answers are available at training. Experimental results show that our method outperformed strong baselines by 1.8 F1 points overall across 10 types of questions of the CSQA dataset. Especially for the “Logical Reasoning” category, our model improves by 7 F1 points. Furthermore, our results are achieved with 90.3% fewer parameters, allowing faster training for large-scale datasets.</abstract>
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%0 Conference Proceedings
%T Knowledge Informed Semantic Parsing for Conversational Question Answering
%A Thirukovalluru, Raghuveer
%A Sridhar, Mukund
%A Thai, Dung
%A Chanumolu, Shruti
%A Monath, Nicholas
%A Ananthakrishnan, Sankaranarayanan
%A McCallum, Andrew
%Y Rogers, Anna
%Y Calixto, Iacer
%Y Vulić, Ivan
%Y Saphra, Naomi
%Y Kassner, Nora
%Y Camburu, Oana-Maria
%Y Bansal, Trapit
%Y Shwartz, Vered
%S Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F thirukovalluru-etal-2021-knowledge
%X Smart assistants are tasked to answer various questions regarding world knowledge. These questions range from retrieval of simple facts to retrieval of complex, multi-hops question followed by various operators (i.e., filter, argmax). Semantic parsing has emerged as the state-of-the-art for answering these kinds of questions by forming queries to extract information from knowledge bases (KBs). Specially, neural semantic parsers (NSPs) effectively translate natural questions to logical forms, which execute on KB and give desirable answers. Yet, NSPs suffer from non-executable logical forms for some instances in the generated logical forms might be missing due to the incompleteness of KBs. Intuitively, knowing the KB structure informs NSP with changes of the global logical forms structures with respect to changes in KB instances. In this work, we propose a novel knowledge-informed decoder variant of NSP. We consider the conversational question answering settings, where a natural language query, its context and its final answers are available at training. Experimental results show that our method outperformed strong baselines by 1.8 F1 points overall across 10 types of questions of the CSQA dataset. Especially for the “Logical Reasoning” category, our model improves by 7 F1 points. Furthermore, our results are achieved with 90.3% fewer parameters, allowing faster training for large-scale datasets.
%R 10.18653/v1/2021.repl4nlp-1.24
%U https://aclanthology.org/2021.repl4nlp-1.24
%U https://doi.org/10.18653/v1/2021.repl4nlp-1.24
%P 231-240
Markdown (Informal)
[Knowledge Informed Semantic Parsing for Conversational Question Answering](https://aclanthology.org/2021.repl4nlp-1.24) (Thirukovalluru et al., RepL4NLP 2021)
ACL
- Raghuveer Thirukovalluru, Mukund Sridhar, Dung Thai, Shruti Chanumolu, Nicholas Monath, Sankaranarayanan Ananthakrishnan, and Andrew McCallum. 2021. Knowledge Informed Semantic Parsing for Conversational Question Answering. In Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pages 231–240, Online. Association for Computational Linguistics.