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Topic Matching in the Wild: Benchmark and Lessons from Real-World ASR Transcripts

2026-08-27 21:55:50
Saman Rahbar, Xiliang Zhu, Irvin Cardoza, David Rossouw
arXiv_AI
                 
Abstract

In contact centers, real-time agent-assist tools determine, for each of many predefined topics, whether a live customer utterance is relevant and display a coaching card to the agent when it is. The input is noisy and challenging: ASR(Automatic Speech Recognition) transcripts of spontaneous phone conversations, which can be unclear, repetitive, and mostly lack punctuation. To systematically study this real-world task, we curate a human-annotated topic-utterance judgments dataset sourced from real call-center transcripts. We compare three types of matchers: a regex-based baseline, zero-shot sentence embedding encoders, and Gemini-based LLM matchers. In addition, two types of topic representations are studied in our benchmark:keyphrases and natural language description. Our empirical experiments highlight the superior performance of lightweight LLM matchers over embedding and regex models when equipped with natural language descriptions.

Abstract (translated)

URL

https://arxiv.org/abs/2609.00330

PDF

https://arxiv.org/pdf/2609.00330.pdf

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