提升语言学习聊天机器人的对话质量：GPT4用于ASR纠错的评估
Enhancing conversational quality in language learning chatbots: An evaluation of GPT4 for ASR error correction
AI总结：
针对语言学习聊天机器人识别非母语口语时WER高、影响对话质量的问题，提出用GPT4做ASR纠错，新增STS、NRS评估指标，发现其虽提升WER却优化对话质量，且无需域内数据即优于标准方法。
摘要：
自然语言处理（NLP）技术融入教育应用已展现出可观成效，在语言学习领域尤为突出。近来，许多开放域口语聊天机器人被用作对话伙伴，帮助语言学习者提升语言技能。然而，一项重大挑战是识别非母语/不流利口语时的词错误率（WER）偏高，这会打断对话流畅性，令学习者感到失望。本文探究了GPT4在对话场景下用于自动语音识别（ASR）纠错的应用。除WER外，我们提出使用语义文本相似度（STS）和下一轮响应合理性（NRS）指标，评估纠错模型对对话质量的影响。我们发现，经GPT4纠错的转录文本尽管WER有所上升，却能带来更高的对话质量。GPT4的表现也优于标准纠错方法，且无需领域内训练数据。
英文摘要：
The integration of natural language processing (NLP) technologies into educational applications has shown promising results, particularly in the language learning domain. Recently, many spoken open-domain chatbots have been used as speaking partners, helping language learners improve their language skills. However, one of the significant challenges is the high word-error-rate (WER) when recognizing non-native/non-fluent speech, which interrupts conversation flow and leads to disappointment for learners. This paper explores the use of GPT4 for ASR error correction in conversational settings. In addition to WER, we propose to use semantic textual similarity (STS) and next response sensibility (NRS) metrics to evaluate the impact of error correction models on the quality of the conversation. We find that transcriptions corrected by GPT4 lead to higher conversation quality, despite an increase in WER. GPT4 also outperforms standard error correction methods without the need for in-domain training data.
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。
