@inproceedings{vincent-etal-2024-reference-less,
title = "Reference-less Analysis of Context Specificity in Translation with Personalised Language Models",
author = "Vincent, Sebastian and
Sumner, Rowanne and
Dowek, Alice and
Prescott, Charlotte and
Preston, Emily and
Bayliss, Chris and
Oakley, Chris and
Scarton, Carolina",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1202",
pages = "13769--13784",
abstract = "Sensitising language models (LMs) to external context helps them to more effectively capture the speaking patterns of individuals with specific characteristics or in particular environments. This work investigates to what extent detailed character and film annotations can be leveraged to personalise LMs in a scalable manner. We then explore the use of such models in evaluating context specificity in machine translation. We build LMs which leverage rich contextual information to reduce perplexity by up to 6.5{\%} compared to a non-contextual model, and generalise well to a scenario with no speaker-specific data, relying on combinations of demographic characteristics expressed via metadata. Our findings are consistent across two corpora, one of which (Cornell-rich) is also a contribution of this paper. We then use our personalised LMs to measure the co-occurrence of extra-textual context and translation hypotheses in a machine translation setting. Our results suggest that the degree to which professional translations in our domain are context-specific can be preserved to a better extent by a contextual machine translation model than a non-contextual model, which is also reflected in the contextual model{'}s superior reference-based scores.",
}
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<abstract>Sensitising language models (LMs) to external context helps them to more effectively capture the speaking patterns of individuals with specific characteristics or in particular environments. This work investigates to what extent detailed character and film annotations can be leveraged to personalise LMs in a scalable manner. We then explore the use of such models in evaluating context specificity in machine translation. We build LMs which leverage rich contextual information to reduce perplexity by up to 6.5% compared to a non-contextual model, and generalise well to a scenario with no speaker-specific data, relying on combinations of demographic characteristics expressed via metadata. Our findings are consistent across two corpora, one of which (Cornell-rich) is also a contribution of this paper. We then use our personalised LMs to measure the co-occurrence of extra-textual context and translation hypotheses in a machine translation setting. Our results suggest that the degree to which professional translations in our domain are context-specific can be preserved to a better extent by a contextual machine translation model than a non-contextual model, which is also reflected in the contextual model’s superior reference-based scores.</abstract>
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%0 Conference Proceedings
%T Reference-less Analysis of Context Specificity in Translation with Personalised Language Models
%A Vincent, Sebastian
%A Sumner, Rowanne
%A Dowek, Alice
%A Prescott, Charlotte
%A Preston, Emily
%A Bayliss, Chris
%A Oakley, Chris
%A Scarton, Carolina
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F vincent-etal-2024-reference-less
%X Sensitising language models (LMs) to external context helps them to more effectively capture the speaking patterns of individuals with specific characteristics or in particular environments. This work investigates to what extent detailed character and film annotations can be leveraged to personalise LMs in a scalable manner. We then explore the use of such models in evaluating context specificity in machine translation. We build LMs which leverage rich contextual information to reduce perplexity by up to 6.5% compared to a non-contextual model, and generalise well to a scenario with no speaker-specific data, relying on combinations of demographic characteristics expressed via metadata. Our findings are consistent across two corpora, one of which (Cornell-rich) is also a contribution of this paper. We then use our personalised LMs to measure the co-occurrence of extra-textual context and translation hypotheses in a machine translation setting. Our results suggest that the degree to which professional translations in our domain are context-specific can be preserved to a better extent by a contextual machine translation model than a non-contextual model, which is also reflected in the contextual model’s superior reference-based scores.
%U https://aclanthology.org/2024.lrec-main.1202
%P 13769-13784
Markdown (Informal)
[Reference-less Analysis of Context Specificity in Translation with Personalised Language Models](https://aclanthology.org/2024.lrec-main.1202) (Vincent et al., LREC-COLING 2024)
ACL
- Sebastian Vincent, Rowanne Sumner, Alice Dowek, Charlotte Prescott, Emily Preston, Chris Bayliss, Chris Oakley, and Carolina Scarton. 2024. Reference-less Analysis of Context Specificity in Translation with Personalised Language Models. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 13769–13784, Torino, Italia. ELRA and ICCL.