记住上下文！通过记忆进行ASR插槽纠错
Remember the context! ASR slot error correction through memorization
摘要：
通过自动语音识别（ASR）系统对槽值（诸如领域特定词或命名实体）的准确识别形成了面向目标的对话系统的核心。虽然它是直接影响下游任务（如语言理解）的关键步骤，但许多领域不可知的ASR系统往往在特定领域或长尾词上表现不佳。它们通常补充有槽纠错系统，但任何神经模型通常很难直接输出这种罕见的实体词。为了解决这个问题，我们提出了k-最近邻（k-NN）搜索，从一个显式的搜索输出特定于域的实体。我们通过在推理过程中使用k-NN搜索来方便地增强预训练的联合音素和基于文本的Transformer序列到序列模型来提高纠错率。我们评估我们提出的方法在五个不同的域包含长尾插槽实体，如全名，机场，街道名称，城市，州。我们表现最好的纠错模型显示，与基线相比，罕见单词实体的单词错误率（WER）相对改善了7.4%，并且在词汇表外（OOV）测试集上也实现了9.8%的相对WER改善。
英文摘要：
Accurate recognition of slot values such as domain specific words or named entities by automatic speech recognition (ASR) systems forms the core of the Goal-oriented Dialogue Systems. Although it is a critical step with direct impact on downstream tasks such as language understanding, many domain agnostic ASR systems tend to perform poorly on domain specific or long tail words. They are often supplemented with slot error correcting systems but it is often hard for any neural model to directly output such rare entity words. To address this problem, we propose k-nearest neighbor (k-NN) search that outputs domain-specific entities from an explicit datastore. We improve error correction rate by conveniently augmenting a pretrained joint phoneme and text based transformer sequence to sequence model with k-NN search during inference. We evaluate our proposed approach on five different domains containing long tail slot entities such as full names, airports, street names, cities, states. Our best performing error correction model shows a relative improvement of 7.4% in word error rate (WER) on rare word entities over the baseline and also achieves a relative WER improvement of 9.8% on an out of vocabulary (OOV) test set.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
