@inproceedings{fan-etal-2017-transfer,
title = "Transfer Learning for Neural Semantic Parsing",
author = "Fan, Xing and
Monti, Emilio and
Mathias, Lambert and
Dreyer, Markus",
editor = "Blunsom, Phil and
Bordes, Antoine and
Cho, Kyunghyun and
Cohen, Shay and
Dyer, Chris and
Grefenstette, Edward and
Hermann, Karl Moritz and
Rimell, Laura and
Weston, Jason and
Yih, Scott",
booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-2607",
doi = "10.18653/v1/W17-2607",
pages = "48--56",
abstract = "The goal of semantic parsing is to map natural language to a machine interpretable meaning representation language (MRL). One of the constraints that limits full exploration of deep learning technologies for semantic parsing is the lack of sufficient annotation training data. In this paper, we propose using sequence-to-sequence in a multi-task setup for semantic parsing with focus on transfer learning. We explore three multi-task architectures for sequence-to-sequence model and compare their performance with the independently trained model. Our experiments show that the multi-task setup aids transfer learning from an auxiliary task with large labeled data to the target task with smaller labeled data. We see an absolute accuracy gain ranging from 1.0{\%} to 4.4{\%} in in our in-house data set and we also see good gains ranging from 2.5{\%} to 7.0{\%} on the ATIS semantic parsing tasks with syntactic and semantic auxiliary tasks.",
}
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<abstract>The goal of semantic parsing is to map natural language to a machine interpretable meaning representation language (MRL). One of the constraints that limits full exploration of deep learning technologies for semantic parsing is the lack of sufficient annotation training data. In this paper, we propose using sequence-to-sequence in a multi-task setup for semantic parsing with focus on transfer learning. We explore three multi-task architectures for sequence-to-sequence model and compare their performance with the independently trained model. Our experiments show that the multi-task setup aids transfer learning from an auxiliary task with large labeled data to the target task with smaller labeled data. We see an absolute accuracy gain ranging from 1.0% to 4.4% in in our in-house data set and we also see good gains ranging from 2.5% to 7.0% on the ATIS semantic parsing tasks with syntactic and semantic auxiliary tasks.</abstract>
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%0 Conference Proceedings
%T Transfer Learning for Neural Semantic Parsing
%A Fan, Xing
%A Monti, Emilio
%A Mathias, Lambert
%A Dreyer, Markus
%Y Blunsom, Phil
%Y Bordes, Antoine
%Y Cho, Kyunghyun
%Y Cohen, Shay
%Y Dyer, Chris
%Y Grefenstette, Edward
%Y Hermann, Karl Moritz
%Y Rimell, Laura
%Y Weston, Jason
%Y Yih, Scott
%S Proceedings of the 2nd Workshop on Representation Learning for NLP
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F fan-etal-2017-transfer
%X The goal of semantic parsing is to map natural language to a machine interpretable meaning representation language (MRL). One of the constraints that limits full exploration of deep learning technologies for semantic parsing is the lack of sufficient annotation training data. In this paper, we propose using sequence-to-sequence in a multi-task setup for semantic parsing with focus on transfer learning. We explore three multi-task architectures for sequence-to-sequence model and compare their performance with the independently trained model. Our experiments show that the multi-task setup aids transfer learning from an auxiliary task with large labeled data to the target task with smaller labeled data. We see an absolute accuracy gain ranging from 1.0% to 4.4% in in our in-house data set and we also see good gains ranging from 2.5% to 7.0% on the ATIS semantic parsing tasks with syntactic and semantic auxiliary tasks.
%R 10.18653/v1/W17-2607
%U https://aclanthology.org/W17-2607
%U https://doi.org/10.18653/v1/W17-2607
%P 48-56
Markdown (Informal)
[Transfer Learning for Neural Semantic Parsing](https://aclanthology.org/W17-2607) (Fan et al., RepL4NLP 2017)
ACL
- Xing Fan, Emilio Monti, Lambert Mathias, and Markus Dreyer. 2017. Transfer Learning for Neural Semantic Parsing. In Proceedings of the 2nd Workshop on Representation Learning for NLP, pages 48–56, Vancouver, Canada. Association for Computational Linguistics.