Euan McGill


2023

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SignON: Sign Language Translation. Progress and challenges.
Vincent Vandeghinste | Dimitar Shterionov | Mirella De Sisto | Aoife Brady | Mathieu De Coster | Lorraine Leeson | Josep Blat | Frankie Picron | Marcello Paolo Scipioni | Aditya Parikh | Louis ten Bosch | John O’Flaherty | Joni Dambre | Jorn Rijckaert | Bram Vanroy | Victor Ubieto Nogales | Santiago Egea Gomez | Ineke Schuurman | Gorka Labaka | Adrián Núnez-Marcos | Irene Murtagh | Euan McGill | Horacio Saggion
Proceedings of the 24th Annual Conference of the European Association for Machine Translation

SignON (https://signon-project.eu/) is a Horizon 2020 project, running from 2021 until the end of 2023, which addresses the lack of technology and services for the automatic translation between sign languages (SLs) and spoken languages, through an inclusive, human-centric solution, hence contributing to the repertoire of communication media for deaf, hard of hearing (DHH) and hearing individuals. In this paper, we present an update of the status of the project, describing the approaches developed to address the challenges and peculiarities of SL machine translation (SLMT).

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BSL-Hansard: A parallel, multimodal corpus of English and interpreted British Sign Language data from parliamentary proceedings
Euan McGill | Horacio Saggion
Proceedings of the Second International Workshop on Automatic Translation for Signed and Spoken Languages

BSL-Hansard is a novel open source and multimodal resource composed by combining Sign Language video data in BSL and English text from the official transcription of British parliamentary sessions. This paper describes the method followed to compile BSL-Hansard including time alignment of text using the MAUS (Schiel, 2015) segmentation system, gives some statistics about this dataset, and suggests experiments. These primarily include end-to-end Sign Language-to-text translation, but is also relevant for broader machine translation, and speech and language processing tasks.

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Part-of-Speech tagging Spanish Sign Language data and its applications in Sign Language machine translation
Euan McGill | Luis Chiruzzo | Santiago Egea Gómez | Horacio Saggion
Proceedings of the Second Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2023)

This paper examines the use of manually part-of-speech tagged sign language gloss data in the Text2Gloss and Gloss2Text translation tasks, as well as running an LSTM-based sequence labelling model on the same glosses for automatic part-of-speech tagging. We find that a combination of tag-enhanced glosses and pretraining the neural model positively impacts performance in the translation tasks. The results of the tagging task are limited, but provide a methodological framework for further research into tagging sign language gloss data.

2022

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Translating Spanish into Spanish Sign Language: Combining Rules and Data-driven Approaches
Luis Chiruzzo | Euan McGill | Santiago Egea-Gómez | Horacio Saggion
Proceedings of the Fifth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2022)

This paper presents a series of experiments on translating between spoken Spanish and Spanish Sign Language glosses (LSE), including enriching Neural Machine Translation (NMT) systems with linguistic features, and creating synthetic data to pretrain and later on finetune a neural translation model. We found evidence that pretraining over a large corpus of LSE synthetic data aligned to Spanish sentences could markedly improve the performance of the translation models.

2021

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Syntax-aware Transformers for Neural Machine Translation: The Case of Text to Sign Gloss Translation
Santiago Egea Gómez | Euan McGill | Horacio Saggion
Proceedings of the 14th Workshop on Building and Using Comparable Corpora (BUCC 2021)

It is well-established that the preferred mode of communication of the deaf and hard of hearing (DHH) community are Sign Languages (SLs), but they are considered low resource languages where natural language processing technologies are of concern. In this paper we study the problem of text to SL gloss Machine Translation (MT) using Transformer-based architectures. Despite the significant advances of MT for spoken languages in the recent couple of decades, MT is in its infancy when it comes to SLs. We enrich a Transformer-based architecture aggregating syntactic information extracted from a dependency parser to word-embeddings. We test our model on a well-known dataset showing that the syntax-aware model obtains performance gains in terms of MT evaluation metrics.