更新神经语义解析器时克服数据冲突

David Gaddy, Alex Kouzemtchenko, Pavankumar Reddy Muddireddy, Prateek Kolhar, Rushi Shah
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引用次数: 1

摘要

在本文中,我们探讨了当某些示例的期望输出发生变化时,如何使用少量新数据来更新面向任务的语义解析模型。当以这种方式进行更新时,出现的一个潜在问题是存在冲突的数据,或者原始训练集中的过时标签。为了评估这个尚未充分研究的问题的影响,我们提出了一个模拟神经语义解析器变化的实验设置。我们展示了冲突数据的存在极大地阻碍了更新的学习,然后探索了几种方法来减轻其影响。与单纯的数据混合策略相比,我们的多任务和数据选择方法大大提高了模型精度,我们的最佳方法缩小了基线和oracle上界之间86%的精度差距。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Overcoming Conflicting Data when Updating a Neural Semantic Parser
In this paper, we explore how to use a small amount of new data to update a task-oriented semantic parsing model when the desired output for some examples has changed. When making updates in this way, one potential problem that arises is the presence of conflicting data, or out-of-date labels in the original training set. To evaluate the impact of this understudied problem, we propose an experimental setup for simulating changes to a neural semantic parser. We show that the presence of conflicting data greatly hinders learning of an update, then explore several methods to mitigate its effect. Our multi-task and data selection methods lead to large improvements in model accuracy compared to a naive data-mixing strategy, and our best method closes 86% of the accuracy gap between this baseline and an oracle upper bound.
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