@inproceedings{bis-etal-2022-paige,
title = "{PAIGE}: Personalized Adaptive Interactions Graph Encoder for Query Rewriting in Dialogue Systems",
author = "Bi{\'s}, Daniel and
Gupta, Saurabh and
Hao, Jie and
Fan, Xing and
Guo, Chenlei",
editor = "Li, Yunyao and
Lazaridou, Angeliki",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = dec,
year = "2022",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-industry.40",
doi = "10.18653/v1/2022.emnlp-industry.40",
pages = "398--408",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T PAIGE: Personalized Adaptive Interactions Graph Encoder for Query Rewriting in Dialogue Systems
%A Biś, Daniel
%A Gupta, Saurabh
%A Hao, Jie
%A Fan, Xing
%A Guo, Chenlei
%Y Li, Yunyao
%Y Lazaridou, Angeliki
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F bis-etal-2022-paige
%X 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.
%R 10.18653/v1/2022.emnlp-industry.40
%U https://aclanthology.org/2022.emnlp-industry.40
%U https://doi.org/10.18653/v1/2022.emnlp-industry.40
%P 398-408
Markdown (Informal)
[PAIGE: Personalized Adaptive Interactions Graph Encoder for Query Rewriting in Dialogue Systems](https://aclanthology.org/2022.emnlp-industry.40) (Biś et al., EMNLP 2022)
ACL