PAIGE: Personalized Adaptive Interactions Graph Encoder for Query Rewriting in Dialogue Systems

Daniel Biś, Saurabh Gupta, Jie Hao, Xing Fan, Chenlei Guo


Abstract
Unexpected responses or repeated clarification questions from conversational agents detract from the users’ experience with technology meant to streamline their daily tasks. To reduce these frictions, Query Rewriting (QR) techniques replace transcripts of faulty queries with alternatives that lead to responses thatsatisfy the users’ needs. Despite their successes, existing QR approaches are limited in their ability to fix queries that require considering users’ personal preferences.We improve QR by proposing Personalized Adaptive Interactions Graph Encoder (PAIGE).PAIGE is the first QR architecture that jointly models user’s affinities and query semantics end-to-end.The core idea is to represent previous user-agent interactions and world knowledge in a structured form — a heterogeneous graph — and apply message passing to propagate latent representations of users’ affinities to refine utterance embeddings.Using these embeddings, PAIGE can potentially provide different rewrites given the same query for users with different preferences. Our model, trained without any human-annotated data, improves the rewrite retrieval precision of state-of-the-art baselines by 12.5–17.5% while having nearly ten times fewer parameters.
Anthology ID:
2022.emnlp-industry.40
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track
Month:
December
Year:
2022
Address:
Abu Dhabi, UAE
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
398–408
Language:
URL:
https://aclanthology.org/2022.emnlp-industry.40
DOI:
Bibkey:
Cite (ACL):
Daniel Biś, Saurabh Gupta, Jie Hao, Xing Fan, and Chenlei Guo. 2022. PAIGE: Personalized Adaptive Interactions Graph Encoder for Query Rewriting in Dialogue Systems. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 398–408, Abu Dhabi, UAE. Association for Computational Linguistics.
Cite (Informal):
PAIGE: Personalized Adaptive Interactions Graph Encoder for Query Rewriting in Dialogue Systems (Biś et al., EMNLP 2022)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.emnlp-industry.40.pdf