arXiv Open Access 2022

Extracting Similar Questions From Naturally-occurring Business Conversations

Xiliang Zhu David Rossouw Shayna Gardiner Simon Corston-Oliver

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Abstrak

Pre-trained contextualized embedding models such as BERT are a standard building block in many natural language processing systems. We demonstrate that the sentence-level representations produced by some off-the-shelf contextualized embedding models have a narrow distribution in the embedding space, and thus perform poorly for the task of identifying semantically similar questions in real-world English business conversations. We describe a method that uses appropriately tuned representations and a small set of exemplars to group questions of interest to business users in a visualization that can be used for data exploration or employee coaching.

Topik & Kata Kunci

cs.CL cs.AI

Penulis (4)

Xiliang Zhu

David Rossouw

Shayna Gardiner

Simon Corston-Oliver

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Zhu, X., Rossouw, D., Gardiner, S., Corston-Oliver, S. (2022). Extracting Similar Questions From Naturally-occurring Business Conversations. https://arxiv.org/abs/2206.01585

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Informasi Jurnal

Tahun Terbit: 2022
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓