1. Paper title
Slot-consistent NLG for Task-oriented Dialogue Systems with Iterative Rectification Network
2. link
https://www.aclweb.org/anthology/2020.acl-main.10.pdf
3. 摘要
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.
4. 要解决什么问题
NLG容易犯的错误：
（1）忽略输入的slot
（2）生成重复的slot
[?] slot是什么？
5. 作者的主要贡献
IRN：bootstrapping + reinforcement
正确生成slot，产生正确流利的响应。
6. 得到了什么结果
slot错误率明显减少。
生成结果得到人工评价的认可。