Continual Dialogue State Tracking via Example-Guided Question Answering
- URL: http://arxiv.org/abs/2305.13721v2
- Date: Thu, 14 Dec 2023 06:58:52 GMT
- Title: Continual Dialogue State Tracking via Example-Guided Question Answering
- Authors: Hyundong Cho, Andrea Madotto, Zhaojiang Lin, Khyathi Raghavi Chandu,
Satwik Kottur, Jing Xu, Jonathan May, Chinnadhurai Sankar
- Abstract summary: We propose reformulating dialogue state tracking as a bundle of granular example-guided question answering tasks.
Our approach alleviates service-specific memorization and teaches a model to contextualize the given question and example.
We find that a model with just 60M parameters can achieve a significant boost by learning to learn from in-context examples retrieved by a retriever trained to identify turns with similar dialogue state changes.
- Score: 48.31523413835549
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Dialogue systems are frequently updated to accommodate new services, but
naively updating them by continually training with data for new services in
diminishing performance on previously learnt services. Motivated by the insight
that dialogue state tracking (DST), a crucial component of dialogue systems
that estimates the user's goal as a conversation proceeds, is a simple natural
language understanding task, we propose reformulating it as a bundle of
granular example-guided question answering tasks to minimize the task shift
between services and thus benefit continual learning. Our approach alleviates
service-specific memorization and teaches a model to contextualize the given
question and example to extract the necessary information from the
conversation. We find that a model with just 60M parameters can achieve a
significant boost by learning to learn from in-context examples retrieved by a
retriever trained to identify turns with similar dialogue state changes.
Combining our method with dialogue-level memory replay, our approach attains
state of the art performance on DST continual learning metrics without relying
on any complex regularization or parameter expansion methods.
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