Topic Matching in the Wild: Benchmark and Lessons from Real-World ASR Transcripts
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Computer Science > Computation and Language
arXiv:2609.00330 (cs)
[Submitted on 27 Aug 2026]
Title:Topic Matching in the Wild: Benchmark and Lessons from Real-World ASR Transcripts
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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.
| Comments: | Accepted at the 11th Workshop on Natural User-generated Text (W-NUT 2026), EMNLP 2026. Camera-ready version. 9 pages, 2 figures, 3 tables |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| ACM classes: | I.2.7; H.3.3 |
| Cite as: | arXiv:2609.00330 [cs.CL] |
| (or arXiv:2609.00330v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.00330
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Topic Matching in the Wild: Benchmark and Lessons from Real-World ASR Transcripts, by Saman Rahbar and 3 other authors
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