Papers by Sebastian Vincent
MTCue: Learning Zero-Shot Control of Extra-Textual Attributes by Leveraging Unstructured Context in Neural Machine Translation (2023.findings-acl)
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| Challenge: | Existing research has focused on providing individual, well-defined types of context in translation, such as the surrounding text or discrete external variables like the speaker’s gender. |
| Approach: | They introduce a novel neural machine translation framework that interprets all context as text. |
| Outcome: | The proposed framework outperforms a baseline that matched the parameters and significantly outperformed it in English translation. |
LOFT: Scalable and More Realistic Long-Context Evaluation (2025.findings-naacl)
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Jinhyuk Lee, Anthony Chen, Zhuyun Dai, Dheeru Dua, Devendra Singh Sachan, Michael Boratko, Yi Luan, Séb Arnold, Vincent Perot, Siddharth Dalmia, Hexiang Hu, Xudong Lin, Panupong Pasupat, Aida Amini, Jeremy R. Cole, Sebastian Riedel, Iftekhar Naim, Ming-Wei Chang, Kelvin Guu
| Challenge: | Long-context language models (LCLMs) can be used to perform tasks traditionally reliant on external tools like retrieval systems or databases. |
| Approach: | They propose a benchmark to evaluate LCLMs' performance on in-context retrieval and reasoning tasks using a set of tokens. |
| Outcome: | The proposed model outperforms state-of-the-art retrieval and RAG systems on in-context retrieval tasks while still requiring prompting strategies. |
Towards Personalised and Document-level Machine Translation of Dialogue (2021.eacl-srw)
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| Challenge: | State-of-the-art (SOTA) neural machine translation systems translate texts at sentence level, ignoring context. |
| Approach: | They propose to integrate extra-textual information into the translation process for the domain of dialogue extracted from TV subtitles in five languages: English, Brazilian Portuguese, German, French and Polish. |
| Outcome: | The proposed systems translate texts at sentence level, ignoring context . there are no readily available robust evaluation metrics for them . |
Reference-less Analysis of Context Specificity in Translation with Personalised Language Models (2024.lrec-main)
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Sebastian Vincent, Rowanne Sumner, Alice Dowek, Charlotte Prescott, Emily Preston, Chris Bayliss, Chris Oakley, Carolina Scarton
| Challenge: | Conventional approaches to NLP tasks build models in a one-size-fits-all fashion disregarding the context of the processed text. |
| Approach: | They build LMs which leverage rich contextual information to reduce perplexity by up to 6.5% compared to a non-contextual model. |
| Outcome: | The proposed models reduce perplexity by up to 6.5% compared to non-contextual models and generalise well to a scenario with no speaker-specific data. |