@inproceedings{li-etal-2020-slot,
title = "Slot-consistent {NLG} for Task-oriented Dialogue Systems with Iterative Rectification Network",
author = "Li, Yangming and
Yao, Kaisheng and
Qin, Libo and
Che, Wanxiang and
Li, Xiaolong and
Liu, Ting",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.10",
doi = "10.18653/v1/2020.acl-main.10",
pages = "97--106",
abstract = "Data-driven approaches using neural networks have achieved promising performances in natural language generation (NLG). However, neural generators are prone to make mistakes, e.g., neglecting an input slot value and generating a redundant slot value. Prior works refer this to hallucination phenomenon. In this paper, we study slot consistency for building reliable NLG systems with all slot values of input dialogue act (DA) properly generated in output sentences. We propose Iterative Rectification Network (IRN) for improving general NLG systems to produce both correct and fluent responses. It applies a bootstrapping algorithm to sample training candidates and uses reinforcement learning to incorporate discrete reward related to slot inconsistency into training. Comprehensive studies have been conducted on multiple benchmark datasets, showing that the proposed methods have significantly reduced the slot error rate (ERR) for all strong baselines. Human evaluations also have confirmed its effectiveness.",
}
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<abstract>Data-driven approaches using neural networks have achieved promising performances in natural language generation (NLG). However, neural generators are prone to make mistakes, e.g., neglecting an input slot value and generating a redundant slot value. Prior works refer this to hallucination phenomenon. In this paper, we study slot consistency for building reliable NLG systems with all slot values of input dialogue act (DA) properly generated in output sentences. We propose Iterative Rectification Network (IRN) for improving general NLG systems to produce both correct and fluent responses. It applies a bootstrapping algorithm to sample training candidates and uses reinforcement learning to incorporate discrete reward related to slot inconsistency into training. Comprehensive studies have been conducted on multiple benchmark datasets, showing that the proposed methods have significantly reduced the slot error rate (ERR) for all strong baselines. Human evaluations also have confirmed its effectiveness.</abstract>
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%0 Conference Proceedings
%T Slot-consistent NLG for Task-oriented Dialogue Systems with Iterative Rectification Network
%A Li, Yangming
%A Yao, Kaisheng
%A Qin, Libo
%A Che, Wanxiang
%A Li, Xiaolong
%A Liu, Ting
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F li-etal-2020-slot
%X Data-driven approaches using neural networks have achieved promising performances in natural language generation (NLG). However, neural generators are prone to make mistakes, e.g., neglecting an input slot value and generating a redundant slot value. Prior works refer this to hallucination phenomenon. In this paper, we study slot consistency for building reliable NLG systems with all slot values of input dialogue act (DA) properly generated in output sentences. We propose Iterative Rectification Network (IRN) for improving general NLG systems to produce both correct and fluent responses. It applies a bootstrapping algorithm to sample training candidates and uses reinforcement learning to incorporate discrete reward related to slot inconsistency into training. Comprehensive studies have been conducted on multiple benchmark datasets, showing that the proposed methods have significantly reduced the slot error rate (ERR) for all strong baselines. Human evaluations also have confirmed its effectiveness.
%R 10.18653/v1/2020.acl-main.10
%U https://aclanthology.org/2020.acl-main.10
%U https://doi.org/10.18653/v1/2020.acl-main.10
%P 97-106
Markdown (Informal)
[Slot-consistent NLG for Task-oriented Dialogue Systems with Iterative Rectification Network](https://aclanthology.org/2020.acl-main.10) (Li et al., ACL 2020)
ACL