@inproceedings{gowriraj-etal-2023-language,
title = "Language-Agnostic Transformers and Assessing {C}hat{GPT}-Based Query Rewriting for Multilingual Document-Grounded {QA}",
author = "Gowriraj, Srinivas and
Tiwari, Soham Dinesh and
Potnis, Mitali and
Bansal, Srijan and
Mitamura, Teruko and
Nyberg, Eric",
editor = "Muresan, Smaranda and
Chen, Vivian and
Casey, Kennington and
David, Vandyke and
Nina, Dethlefs and
Koji, Inoue and
Erik, Ekstedt and
Stefan, Ultes",
booktitle = "Proceedings of the Third DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.dialdoc-1.11",
doi = "10.18653/v1/2023.dialdoc-1.11",
pages = "101--108",
abstract = "The DialDoc 2023 shared task has expanded the document-grounded dialogue task to encompass multiple languages, despite having limited annotated data. This paper assesses the effectiveness of both language-agnostic and language-aware paradigms for multilingual pre-trained transformer models in a bi-encoder-based dense passage retriever (DPR), concluding that the language-agnostic approach is superior. Additionally, the study investigates the impact of query rewriting techniques using large language models, such as ChatGPT, on multilingual, document-grounded question-answering systems. The experiments conducted demonstrate that, for the examples examined, query rewriting does not enhance performance compared to the original queries. This failure is due to topic switching in final dialogue turns and irrelevant topics being considered for query rewriting.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="gowriraj-etal-2023-language">
<titleInfo>
<title>Language-Agnostic Transformers and Assessing ChatGPT-Based Query Rewriting for Multilingual Document-Grounded QA</title>
</titleInfo>
<name type="personal">
<namePart type="given">Srinivas</namePart>
<namePart type="family">Gowriraj</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Soham</namePart>
<namePart type="given">Dinesh</namePart>
<namePart type="family">Tiwari</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mitali</namePart>
<namePart type="family">Potnis</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Srijan</namePart>
<namePart type="family">Bansal</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Teruko</namePart>
<namePart type="family">Mitamura</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Eric</namePart>
<namePart type="family">Nyberg</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2023-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Third DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering</title>
</titleInfo>
<name type="personal">
<namePart type="given">Smaranda</namePart>
<namePart type="family">Muresan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Vivian</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kennington</namePart>
<namePart type="family">Casey</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Vandyke</namePart>
<namePart type="family">David</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dethlefs</namePart>
<namePart type="family">Nina</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Inoue</namePart>
<namePart type="family">Koji</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ekstedt</namePart>
<namePart type="family">Erik</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ultes</namePart>
<namePart type="family">Stefan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Toronto, Canada</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>The DialDoc 2023 shared task has expanded the document-grounded dialogue task to encompass multiple languages, despite having limited annotated data. This paper assesses the effectiveness of both language-agnostic and language-aware paradigms for multilingual pre-trained transformer models in a bi-encoder-based dense passage retriever (DPR), concluding that the language-agnostic approach is superior. Additionally, the study investigates the impact of query rewriting techniques using large language models, such as ChatGPT, on multilingual, document-grounded question-answering systems. The experiments conducted demonstrate that, for the examples examined, query rewriting does not enhance performance compared to the original queries. This failure is due to topic switching in final dialogue turns and irrelevant topics being considered for query rewriting.</abstract>
<identifier type="citekey">gowriraj-etal-2023-language</identifier>
<identifier type="doi">10.18653/v1/2023.dialdoc-1.11</identifier>
<location>
<url>https://aclanthology.org/2023.dialdoc-1.11</url>
</location>
<part>
<date>2023-07</date>
<extent unit="page">
<start>101</start>
<end>108</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Language-Agnostic Transformers and Assessing ChatGPT-Based Query Rewriting for Multilingual Document-Grounded QA
%A Gowriraj, Srinivas
%A Tiwari, Soham Dinesh
%A Potnis, Mitali
%A Bansal, Srijan
%A Mitamura, Teruko
%A Nyberg, Eric
%Y Muresan, Smaranda
%Y Chen, Vivian
%Y Casey, Kennington
%Y David, Vandyke
%Y Nina, Dethlefs
%Y Koji, Inoue
%Y Erik, Ekstedt
%Y Stefan, Ultes
%S Proceedings of the Third DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F gowriraj-etal-2023-language
%X The DialDoc 2023 shared task has expanded the document-grounded dialogue task to encompass multiple languages, despite having limited annotated data. This paper assesses the effectiveness of both language-agnostic and language-aware paradigms for multilingual pre-trained transformer models in a bi-encoder-based dense passage retriever (DPR), concluding that the language-agnostic approach is superior. Additionally, the study investigates the impact of query rewriting techniques using large language models, such as ChatGPT, on multilingual, document-grounded question-answering systems. The experiments conducted demonstrate that, for the examples examined, query rewriting does not enhance performance compared to the original queries. This failure is due to topic switching in final dialogue turns and irrelevant topics being considered for query rewriting.
%R 10.18653/v1/2023.dialdoc-1.11
%U https://aclanthology.org/2023.dialdoc-1.11
%U https://doi.org/10.18653/v1/2023.dialdoc-1.11
%P 101-108
Markdown (Informal)
[Language-Agnostic Transformers and Assessing ChatGPT-Based Query Rewriting for Multilingual Document-Grounded QA](https://aclanthology.org/2023.dialdoc-1.11) (Gowriraj et al., dialdoc 2023)
ACL