@inproceedings{richardson-etal-2023-learning,
title = "Learning to Retrieve Engaging Follow-Up Queries",
author = "Richardson, Christopher and
Kar, Sudipta and
Kumar, Anjishnu and
Ramachandran, Anand and
Raeesy, Zeynab and
Khan, Omar and
Sethy, Abhinav",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.149",
doi = "10.18653/v1/2023.findings-eacl.149",
pages = "2009--2016",
abstract = "Open domain conversational agents can answer a broad range of targeted queries. However, the sequential nature of interaction with these systems makes knowledge exploration a lengthy task which burdens the user with asking a chain of well phrased questions. In this paper, we present a retrieval based system and associated dataset for predicting the next questions that the user might have. Such a system can proactively assist users in knowledge exploration leading to a more engaging dialog. The retrieval system is trained on a dataset called the Follow-up Query Bank (FQ-Bank). FQ-Bank contains {\textasciitilde}14K multi-turn information-seeking conversations with a valid follow-up question and a set of invalid candidates. The invalid candidates are generated to simulate various syntactic and semantic confounders such as paraphrases, partial entity match, irrelevant entity, and ASR errors. We use confounder specific techniques to simulate these negative examples on the OR-QuAC dataset. Then, we train ranking models on FQ-Bank and present results comparing supervised and unsupervised approaches. The results suggest that we can retrieve the valid follow-ups by ranking them in higher positions compared to confounders, but further knowledge grounding can improve ranking performance.FQ-Bank is publicly available at \url{https://github.com/amazon-science/fq-bank}.",
}
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<abstract>Open domain conversational agents can answer a broad range of targeted queries. However, the sequential nature of interaction with these systems makes knowledge exploration a lengthy task which burdens the user with asking a chain of well phrased questions. In this paper, we present a retrieval based system and associated dataset for predicting the next questions that the user might have. Such a system can proactively assist users in knowledge exploration leading to a more engaging dialog. The retrieval system is trained on a dataset called the Follow-up Query Bank (FQ-Bank). FQ-Bank contains ~14K multi-turn information-seeking conversations with a valid follow-up question and a set of invalid candidates. The invalid candidates are generated to simulate various syntactic and semantic confounders such as paraphrases, partial entity match, irrelevant entity, and ASR errors. We use confounder specific techniques to simulate these negative examples on the OR-QuAC dataset. Then, we train ranking models on FQ-Bank and present results comparing supervised and unsupervised approaches. The results suggest that we can retrieve the valid follow-ups by ranking them in higher positions compared to confounders, but further knowledge grounding can improve ranking performance.FQ-Bank is publicly available at https://github.com/amazon-science/fq-bank.</abstract>
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%0 Conference Proceedings
%T Learning to Retrieve Engaging Follow-Up Queries
%A Richardson, Christopher
%A Kar, Sudipta
%A Kumar, Anjishnu
%A Ramachandran, Anand
%A Raeesy, Zeynab
%A Khan, Omar
%A Sethy, Abhinav
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F richardson-etal-2023-learning
%X Open domain conversational agents can answer a broad range of targeted queries. However, the sequential nature of interaction with these systems makes knowledge exploration a lengthy task which burdens the user with asking a chain of well phrased questions. In this paper, we present a retrieval based system and associated dataset for predicting the next questions that the user might have. Such a system can proactively assist users in knowledge exploration leading to a more engaging dialog. The retrieval system is trained on a dataset called the Follow-up Query Bank (FQ-Bank). FQ-Bank contains ~14K multi-turn information-seeking conversations with a valid follow-up question and a set of invalid candidates. The invalid candidates are generated to simulate various syntactic and semantic confounders such as paraphrases, partial entity match, irrelevant entity, and ASR errors. We use confounder specific techniques to simulate these negative examples on the OR-QuAC dataset. Then, we train ranking models on FQ-Bank and present results comparing supervised and unsupervised approaches. The results suggest that we can retrieve the valid follow-ups by ranking them in higher positions compared to confounders, but further knowledge grounding can improve ranking performance.FQ-Bank is publicly available at https://github.com/amazon-science/fq-bank.
%R 10.18653/v1/2023.findings-eacl.149
%U https://aclanthology.org/2023.findings-eacl.149
%U https://doi.org/10.18653/v1/2023.findings-eacl.149
%P 2009-2016
Markdown (Informal)
[Learning to Retrieve Engaging Follow-Up Queries](https://aclanthology.org/2023.findings-eacl.149) (Richardson et al., Findings 2023)
ACL
- Christopher Richardson, Sudipta Kar, Anjishnu Kumar, Anand Ramachandran, Zeynab Raeesy, Omar Khan, and Abhinav Sethy. 2023. Learning to Retrieve Engaging Follow-Up Queries. In Findings of the Association for Computational Linguistics: EACL 2023, pages 2009–2016, Dubrovnik, Croatia. Association for Computational Linguistics.