Intent Classification (IC) and Slot Labeling (SL) models, which form the basis of dialogue systems, often encounter noisy data in real-word environments. In this work, we investigate how robust IC/SL models are to noisy data. We collect and publicly release a test-suite for seven common noise types found in production human-to-bot conversations (abbreviations, casing, misspellings, morphological variants, paraphrases, punctuation and synonyms). On this test-suite, we show that common noise types substantially degrade the IC accuracy and SL F1 performance of state-of-the-art BERT-based IC/SL models. By leveraging cross-noise robustness transfer -- training on one noise type to improve robustness on another noise type -- we design aggregate data-augmentation approaches that increase the model performance across all seven noise types by +10.8% for IC accuracy and +15 points for SL F1 on average. To the best of our knowledge, this is the first work to present a single IC/SL model that is robust to a wide range of noise phenomena.
翻译:构成对话系统基础的本级分类(IC)和Slot Labeling(SL)模型,这些模型是对话系统的基础,在现实环境中常常遇到噪音数据。在这项工作中,我们调查IC/SL模型对噪音数据是如何强大的。我们收集和公开发布一个测试工具,用于生产人对人对话中的七种常见噪音类型(浮标、外壳、弹壳、错译、形态变异、副词句、标语、标语和同义词)。在这个测试中,我们显示,常见噪音类型大大降低了IC的准确性,并大大降低了基于IC/SL/SL的最新BERT/SL型模型的SL F1性能。通过利用交叉噪声稳健性传输(关于一种噪音的培训以提高另一种噪音类型的稳健性),我们设计了综合数据提示方法,将所有七种噪音类型的模型性能提高+10.8%,用于IC准确性,而SL1的+15点平均值。据我们所知,这是首次推出一个统一的IC/SL模型,以稳健度为一种。