| Challenge: | Spoken language glossification (SLG) aims to translate spoken language text into sign language gloss, i.e., written record of sign language. |
| Approach: | They propose a framework to translate spoken language into a sign language gloss . they use monolingual spoken language text to integrate it into training . |
| Outcome: | The proposed framework incorporates large-scale monolingual spoken language text into SLG training. |
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Sign Language Translation with Sentence Embedding Supervision (2024.acl-short)
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| Challenge: | State-of-the-art sign language translation systems facilitate learning through gloss annotations when available at scale. |
| Approach: | They propose to use sentence embeddings of the target sentences at training time that take the role of glosses to supervise the learning process. |
| Outcome: | The proposed method significantly outperforms gloss-free approaches on German and American sign languages and with mono- and multilingual sentence embeddings and translation systems. |
Multilingual Gloss-free Sign Language Translation: Towards Building a Sign Language Foundation Model (2025.acl-short)
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| Challenge: | Existing studies focus on translating a single SL into a spoken language (one-to-one SLT) however, multilingual SLT remains unexplored due to language conflicts and alignment difficulties across SLs and spoken languages. |
| Approach: | They propose a multilingual gloss-free model that can be used to translate a single SL into a spoken language and generate a token-level SL identification and spoken text. |
| Outcome: | The proposed model supports 10 SLs and handles one-to-one, many-to-1, and many- to-many SLT tasks. |
Gloss2Text: Sign Language Gloss translation using LLMs and Semantically Aware Label Smoothing (2024.findings-emnlp)
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| Challenge: | Existing approaches to sign language translation use gloss annotations as an intermediary . a new approach to use large language models and word embeddings to improve Gloss2Text translation is needed. |
| Approach: | They propose to leverage large language models pre-trained on expansive and diverse corpora to improve Gloss2Text translation stage by using data augmentation and label-smoothing loss function. |
| Outcome: | The proposed approach surpasses state-of-the-art methods on the PHOENIX Weather 2014T dataset . it shows that gloss annotations can be used to guide the translation process . |
WLASL-LEX: a Dataset for Recognising Phonological Properties in American Sign Language (2022.acl-short)
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| Challenge: | Signed Language Processing (SLP) is a major form of NLP, but has been overlooked by the NLP community. |
| Approach: | They leverage existing resources to construct a large-scale dataset of American Sign Language signs annotated with six different phonological properties. |
| Outcome: | The proposed model outperforms existing approaches on signs unobserved during training. |
Explore More Guidance: A Task-aware Instruction Network for Sign Language Translation Enhanced with Data Augmentation (2022.findings-naacl)
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| Challenge: | Existing studies focus on the recognition step, while paying less attention to sign language translation. |
| Approach: | They propose a task-aware instruction network, namely TIN-SLT, for sign language translation, by introducing the isntruction module and the learning-based feature fuse strategy into a Transformer network. |
| Outcome: | The proposed system outperforms existing solutions on two benchmark datasets, PHOENIX-2014-T and ASLG-PC12, and outperformed previous best solutions by 1.65 and 1.42 in terms of BLEU-4. |
Neural Machine Translation Methods for Translating Text to Sign Language Glosses (2023.acl-long)
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| Challenge: | State-of-the-art techniques common to low resource Machine Translation (MT) are applied to improve MT of spoken language text to Sign Language glosses. |
| Approach: | They propose to use data augmentation, semi-supervised Neural Machine Translation, transfer learning and multilingual NMT to improve MT of spoken language to Sign Language glosses. |
| Outcome: | The proposed models outperform previous work on two German SL corpora and are confirmed by human evaluation. |
Better Sign Language Translation with STMC-Transformer (2020.coling-main)
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| Challenge: | Current SLT approaches use a sign language recognition system to extract sign language glosses from videos. |
| Approach: | They propose to use a Sign Language Recognition system to extract sign language glosses from videos and a translation system to generate spoken language translations from the glossed sign language. |
| Outcome: | The proposed system outperforms existing methods on gloss-to-text and video-to text translations on the ASLG-PC12 corpus. |
Automatic Gloss Dictionary for Sign Language Learners (2022.acl-demo)
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| Challenge: | 430 million people worldwide have developed hearing loss and 700 million more are learning a sign language as a second language . sign language learners have limited means of seeking assistance and are restricted to class offerings or relying on a webcam to look up the sign. |
| Approach: | They propose an online tool supporting 2, 000 signs to assist language learners in determining the meaning of given signs. |
| Outcome: | The proposed system can lower the barrier in sign language learning by addressing the common problem of sign finding and make it accessible to the wider community. |
GlossLM: A Massively Multilingual Corpus and Pretrained Model for Interlinear Glossed Text (2024.emnlp-main)
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| Challenge: | Existing resources for standardized, easily accessible IGT data limit their applicability to linguistic research. |
| Approach: | They compile the largest existing corpus of interlinear glossed text data from a variety of sources and use it to generate annotated text. |
| Outcome: | The proposed model outperforms SOTA models on monolingual corpora by 6.6%. |
Gloss-Free End-to-End Sign Language Translation (2023.acl-long)
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| Challenge: | a study of sign language translation without gloss annotations focuses on the problem of gloss annotation . gloss annotation is hard to acquire, especially in large quantities, and limits the domain coverage of translation datasets . |
| Approach: | They propose a gloss-free end-to-end sign language translation framework to solve this problem . gloss annotations are hard to acquire, especially in large quantities, they argue . |
| Outcome: | The proposed framework improves sign language translation performance on large-scale datasets . gloss annotations are hard to acquire, especially in large quantities . |