Extracting Similar Questions From Naturally-occurring Business Conversations

• URL: http://arxiv.org/abs/2206.01585v1
• Date: Fri, 3 Jun 2022 14:13:44 GMT
• Title: Extracting Similar Questions From Naturally-occurring Business Conversations
• Authors: Xiliang Zhu, David Rossouw, Shayna Gardiner, Simon Corston-Oliver
• Abstract summary: We show that some off-the-shelf contextualized embedding models have a narrow distribution in the embedding space, and 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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