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Few Shot Dialogue State Tracking using Meta-learning ...
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Abstract:
Dialogue State Tracking (DST) forms a core component of automated chatbot-based systems designed for specific goals like hotel, taxi reservation, tourist information, etc. With the increasing need to deploy such systems in new domains, solving the problem of zero/few-shot DST has become necessary. There has been a rising trend for learning to transfer knowledge from resource-rich domains to unknown domains with minimal need for additional data. In this work, we explore the merits of meta-learning algorithms for this transfer and hence, propose a meta-learner D-REPTILE specific to the DST problem. With extensive experimentation, we provide clear evidence of benefits over conventional approaches across different domains, methods, base models, and datasets with significant (5-25%) improvement over the baseline in a low-data setting. Our proposed meta-learner is agnostic of the underlying model and hence any existing state-of-the-art DST system can improve its performance on unknown domains using our training ...
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Keyword:
Computational Linguistics; Condensed Matter Physics; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
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URL: https://underline.io/lecture/29956-few-shot-dialogue-state-tracking-using-meta-learning
https://dx.doi.org/10.48448/t56q-rc48
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