Exploring the Robustness of Task-oriented Dialogue Systems for Colloquial German Varieties

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A4NWUYUBW" target="_blank" >RIV/00216208:11320/25:4NWUYUBW - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189928163&partnerID=40&md5=a91d3dfa7ab3a32f6a6ef40c631b530c" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189928163&partnerID=40&md5=a91d3dfa7ab3a32f6a6ef40c631b530c</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Exploring the Robustness of Task-oriented Dialogue Systems for Colloquial German Varieties

Original language description

Mainstream cross-lingual task-oriented dialogue (ToD) systems leverage the transfer learning paradigm by training a joint model for intent recognition and slot-filling in English and applying it, zero-shot, to other languages. We address a gap in prior research, which often overlooked the transfer to lower-resource colloquial varieties due to limited test data. Inspired by prior work on English varieties, we craft and manually evaluate perturbation rules that transform German sentences into colloquial forms and use them to synthesize test sets in four ToD datasets. Our perturbation rules cover 18 distinct language phenomena, enabling us to explore the impact of each perturbation on slot and intent performance. Using these new datasets, we conduct an experimental evaluation across six different transformers. Here, we demonstrate that when applied to colloquial varieties, ToD systems maintain their intent recognition performance, losing 6% (4.62 percentage points) in accuracy on average. However, they exhibit a significant drop in slot detection, with a decrease of 31% (21 percentage points) in slot F1 score. Our findings are further supported by a transfer experiment from Standard American English to synthetic Urban African American Vernacular English. © 2024 Association for Computational Linguistics.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

EACL - Conf. European Chapter Assoc. Comput. Linguist., Proc. Conf.

ISBN

979-889176088-2

ISSN

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e-ISSN

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Number of pages

24

Pages from-to

445-468

Publisher name

Association for Computational Linguistics (ACL)

Place of publication

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Event location

St. Julian's

Event date

Jan 1, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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