@inproceedings{gao-etal-2025-interpretable,
title = "An Interpretable and Crosslingual Method for Evaluating Second-Language Dialogues",
author = "Gao, Rena and
Wu, Jingxuan and
Wu, Xuetong and
Roever, Carsten and
Wu, Jing and
Lv, Long and
Lau, Jey Han",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.100/",
doi = "10.18653/v1/2025.naacl-long.100",
pages = "1979--2008",
ISBN = "979-8-89176-189-6",
abstract = "We analyse the cross-lingual transferability of a dialogue evaluation framework that assesses the relationships between micro-level linguistic features (e.g. backchannels) and macro-level interactivity labels (e.g. topic management), originally designed for English-as-a-second-language dialogues. To this end, we develop CNIMA (**C**hinese **N**on-Native **I**nteractivity **M**easurement and **A**utomation), a Chinese-as-a-second-language labelled dataset with 10K dialogues. We found the evaluation framework to be robust across languages, revealing language-specific and language-universal relationships between micro-level and macro-level features. Next, we propose an automated, interpretable approach with low data requirements that scores the overall quality of a second-language dialogue based on the framework. Our approach is interpretable in that it reveals the key linguistic and interactivity features that contributed to the overall quality score. As our approach does not require labelled data, it can also be adapted to other languages for second-language dialogue evaluation."
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<abstract>We analyse the cross-lingual transferability of a dialogue evaluation framework that assesses the relationships between micro-level linguistic features (e.g. backchannels) and macro-level interactivity labels (e.g. topic management), originally designed for English-as-a-second-language dialogues. To this end, we develop CNIMA (**C**hinese **N**on-Native **I**nteractivity **M**easurement and **A**utomation), a Chinese-as-a-second-language labelled dataset with 10K dialogues. We found the evaluation framework to be robust across languages, revealing language-specific and language-universal relationships between micro-level and macro-level features. Next, we propose an automated, interpretable approach with low data requirements that scores the overall quality of a second-language dialogue based on the framework. Our approach is interpretable in that it reveals the key linguistic and interactivity features that contributed to the overall quality score. As our approach does not require labelled data, it can also be adapted to other languages for second-language dialogue evaluation.</abstract>
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%0 Conference Proceedings
%T An Interpretable and Crosslingual Method for Evaluating Second-Language Dialogues
%A Gao, Rena
%A Wu, Jingxuan
%A Wu, Xuetong
%A Roever, Carsten
%A Wu, Jing
%A Lv, Long
%A Lau, Jey Han
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F gao-etal-2025-interpretable
%X We analyse the cross-lingual transferability of a dialogue evaluation framework that assesses the relationships between micro-level linguistic features (e.g. backchannels) and macro-level interactivity labels (e.g. topic management), originally designed for English-as-a-second-language dialogues. To this end, we develop CNIMA (**C**hinese **N**on-Native **I**nteractivity **M**easurement and **A**utomation), a Chinese-as-a-second-language labelled dataset with 10K dialogues. We found the evaluation framework to be robust across languages, revealing language-specific and language-universal relationships between micro-level and macro-level features. Next, we propose an automated, interpretable approach with low data requirements that scores the overall quality of a second-language dialogue based on the framework. Our approach is interpretable in that it reveals the key linguistic and interactivity features that contributed to the overall quality score. As our approach does not require labelled data, it can also be adapted to other languages for second-language dialogue evaluation.
%R 10.18653/v1/2025.naacl-long.100
%U https://aclanthology.org/2025.naacl-long.100/
%U https://doi.org/10.18653/v1/2025.naacl-long.100
%P 1979-2008
Markdown (Informal)
[An Interpretable and Crosslingual Method for Evaluating Second-Language Dialogues](https://aclanthology.org/2025.naacl-long.100/) (Gao et al., NAACL 2025)
ACL
- Rena Gao, Jingxuan Wu, Xuetong Wu, Carsten Roever, Jing Wu, Long Lv, and Jey Han Lau. 2025. An Interpretable and Crosslingual Method for Evaluating Second-Language Dialogues. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 1979–2008, Albuquerque, New Mexico. Association for Computational Linguistics.