mlsanityData to 14 Oct 2026

Adapting Task-Oriented Dialogue Models for Email Conversations

arXiv 2208.09439 ↗First posted 19 Aug 2022cs.CLcited by 1

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Abstract (arXiv)

Intent detection is a key part of any Natural Language Understanding (NLU) system of a conversational assistant. Detecting the correct intent is essential yet difficult for email conversations where multiple directives and intents are present. In such settings, conversation context can become a key disambiguating factor for detecting the user's request from the assistant. One prominent way of incorporating context is modeling past conversation history like task-oriented dialogue models. However, the nature of email conversations (long form) restricts direct usage of the latest advances in task-oriented dialogue models. So in this paper, we provide an effective transfer learning framework (EMToD) that allows the latest development in dialogue models to be adapted for long-form conversations. We show that the proposed EMToD framework improves intent detection performance over pre-trained language models by 45% and over pre-trained dialogue models by 30% for task-oriented email conversations. Additionally, the modular nature of the proposed framework allows plug-and-play for any future developments in both pre-trained language and task-oriented dialogue models.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 24 of its 32 references that are in the map. First posted on arXiv 19 Aug 2022.

Builds on

24 of 32 references.

References · 24

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12 more
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  1. 2023MAILEX: Email Event and Argument Extractioncited by 6