Sentence Retrieval for Open-Ended Dialogue Using Dual Contextual Modeling

Itay Harel, Hagai Taitelbaum, Idan Szpektor, Oren Kurland

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


We address the task of retrieving sentences for an open domain dialogue that contain information useful for generating the next turn. We propose several novel neural retrieval architectures based on dual contextual modeling: the dialogue context and the context of the sentence in its ambient document. The architectures utilize contextualized language models (BERT), fine-tuned on a large-scale dataset constructed from Reddit. We evaluate the models using a recently published dataset. The performance of our most effective model is substantially superior to that of strong baselines.

Original languageEnglish
Title of host publicationAdvances in Information Retrieval - 45th European Conference on Information Retrieval, ECIR 2023, Proceedings
EditorsJaap Kamps, Lorraine Goeuriot, Fabio Crestani, Maria Maistro, Hideo Joho, Brian Davis, Cathal Gurrin, Annalina Caputo, Udo Kruschwitz
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages15
ISBN (Print)9783031282430
StatePublished - 2023
Externally publishedYes
Event45th European Conference on Information Retrieval, ECIR 2023 - Dublin, Ireland
Duration: 2 Apr 20236 Apr 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13980 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference45th European Conference on Information Retrieval, ECIR 2023

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.


Acknowledgements. We thank the reviewers for their comments. This work was supported in part by a grant from Google.

FundersFunder number


    • Dialogue retrieval
    • Open domain dialogue
    • Sentence retrieval


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