@inproceedings{artemova-etal-2024-exploring,
title = "Exploring the Robustness of Task-oriented Dialogue Systems for Colloquial {G}erman Varieties",
author = "Artemova, Ekaterina and
Blaschke, Verena and
Plank, Barbara",
editor = "Graham, Yvette and
Purver, Matthew",
booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = mar,
year = "2024",
address = "St. Julian{'}s, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.eacl-long.28",
pages = "445--468",
abstract = "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 F$_1$ score.Our findings are further supported by a transfer experiment from Standard American English to synthetic Urban African American Vernacular English.",
}
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%0 Conference Proceedings
%T Exploring the Robustness of Task-oriented Dialogue Systems for Colloquial German Varieties
%A Artemova, Ekaterina
%A Blaschke, Verena
%A Plank, Barbara
%Y Graham, Yvette
%Y Purver, Matthew
%S Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F artemova-etal-2024-exploring
%X 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.
%U https://aclanthology.org/2024.eacl-long.28
%P 445-468
Markdown (Informal)
[Exploring the Robustness of Task-oriented Dialogue Systems for Colloquial German Varieties](https://aclanthology.org/2024.eacl-long.28) (Artemova et al., EACL 2024)
ACL