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20212024
most citedControlling Extra-Textual Attributes about Dialogue Participants -- A Case Study of English-to-Polish Neural Machine Translation

1 citations · 2 across the 5 of their papers we have counts for

collaborators

6 papers

cs.LG2024★ 1 cited

A Case Study on Contextual Machine Translation in a Professional Scenario of Subtitling

Sebastian Vincent, Charlotte Prescott, Chris Bayliss +2

Incorporating extra-textual context such as film metadata into the machine translation (MT) pipeline can enhance translation quality, as indicated by automatic evaluation in recent…

cs.CL2023

MTCue: Learning Zero-Shot Control of Extra-Textual Attributes by Leveraging Unstructured Context in Neural Machine Translation

Sebastian Vincent, Robert Flynn, Carolina Scarton

Efficient utilisation of both intra- and extra-textual context remains one of the critical gaps between machine and human translation. Existing research has primarily focused on pr…

cs.CL2023

Reference-less Analysis of Context Specificity in Translation with Personalised Language Models

Sebastian Vincent, Alice Dowek, Rowanne Sumner +5

Sensitising language models (LMs) to external context helps them to more effectively capture the speaking patterns of individuals with specific characteristics or in particular env…

cs.CL2022★ 1 cited

Controlling Extra-Textual Attributes about Dialogue Participants -- A Case Study of English-to-Polish Neural Machine Translation

Sebastian T. Vincent, Loïc Barrault, Carolina Scarton

Unlike English, morphologically rich languages can reveal characteristics of speakers or their conversational partners, such as gender and number, via pronouns, morphological endin…

cs.CL2022

Controlling Formality in Low-Resource NMT with Domain Adaptation and Re-Ranking: SLT-CDT-UoS at IWSLT2022

Sebastian T. Vincent, Loïc Barrault, Carolina Scarton

This paper describes the SLT-CDT-UoS group's submission to the first Special Task on Formality Control for Spoken Language Translation, part of the IWSLT 2022 Evaluation Campaign.…

cs.CL2021

Towards Personalised and Document-level Machine Translation of Dialogue

Sebastian T. Vincent

State-of-the-art (SOTA) neural machine translation (NMT) systems translate texts at sentence level, ignoring context: intra-textual information, like the previous sentence, and ext…