Select and Reorder: A Novel Approach for Neural Sign Language Production
- URL: http://arxiv.org/abs/2404.11532v1
- Date: Wed, 17 Apr 2024 16:25:19 GMT
- Title: Select and Reorder: A Novel Approach for Neural Sign Language Production
- Authors: Harry Walsh, Ben Saunders, Richard Bowden,
- Abstract summary: Sign languages, often categorised as low-resource languages, face significant challenges in achieving accurate translation.
This paper introduces Select and Reorder (S&R), a novel approach that addresses data scarcity by breaking down the translation process into two distinct steps: Gloss Selection (GS) and Gloss Reordering (GR)
We achieve state-of-the-art BLEU and Rouge scores on the Meine DGS Annotated (mDGS) dataset, demonstrating a substantial BLUE-1 improvement of 37.88% in Text to Gloss (T2G) Translation.
- Score: 35.35777909051466
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Sign languages, often categorised as low-resource languages, face significant challenges in achieving accurate translation due to the scarcity of parallel annotated datasets. This paper introduces Select and Reorder (S&R), a novel approach that addresses data scarcity by breaking down the translation process into two distinct steps: Gloss Selection (GS) and Gloss Reordering (GR). Our method leverages large spoken language models and the substantial lexical overlap between source spoken languages and target sign languages to establish an initial alignment. Both steps make use of Non-AutoRegressive (NAR) decoding for reduced computation and faster inference speeds. Through this disentanglement of tasks, we achieve state-of-the-art BLEU and Rouge scores on the Meine DGS Annotated (mDGS) dataset, demonstrating a substantial BLUE-1 improvement of 37.88% in Text to Gloss (T2G) Translation. This innovative approach paves the way for more effective translation models for sign languages, even in resource-constrained settings.
Related papers
- Chain-of-Translation Prompting (CoTR): A Novel Prompting Technique for Low Resource Languages [0.4499833362998489]
Chain of Translation Prompting (CoTR) is a novel strategy designed to enhance the performance of language models in low-resource languages.
CoTR restructures prompts to first translate the input context from a low-resource language into a higher-resource language, such as English.
We demonstrate the effectiveness of this method through a case study on the low-resource Indic language Marathi.
arXiv Detail & Related papers (2024-09-06T17:15:17Z) - Gloss-free Sign Language Translation: Improving from Visual-Language
Pretraining [56.26550923909137]
Gloss-Free Sign Language Translation (SLT) is a challenging task due to its cross-domain nature.
We propose a novel Gloss-Free SLT based on Visual-Language Pretraining (GFSLT-)
Our approach involves two stages: (i) integrating Contrastive Language-Image Pre-training with masked self-supervised learning to create pre-tasks that bridge the semantic gap between visual and textual representations and restore masked sentences, and (ii) constructing an end-to-end architecture with an encoder-decoder-like structure that inherits the parameters of the pre-trained Visual and Text Decoder from
arXiv Detail & Related papers (2023-07-27T10:59:18Z) - Better Sign Language Translation with Monolingual Data [6.845232643246564]
Sign language translation (SLT) systems heavily relies on the availability of large-scale parallel G2T pairs.
This paper proposes a simple and efficient rule transformation method to transcribe the large-scale target monolingual data into its pseudo glosses automatically.
Empirical results show that the proposed approach can significantly improve the performance of SLT.
arXiv Detail & Related papers (2023-04-21T09:39:54Z) - CROP: Zero-shot Cross-lingual Named Entity Recognition with Multilingual
Labeled Sequence Translation [113.99145386490639]
Cross-lingual NER can transfer knowledge between languages via aligned cross-lingual representations or machine translation results.
We propose a Cross-lingual Entity Projection framework (CROP) to enable zero-shot cross-lingual NER.
We adopt a multilingual labeled sequence translation model to project the tagged sequence back to the target language and label the target raw sentence.
arXiv Detail & Related papers (2022-10-13T13:32:36Z) - A Dual-Contrastive Framework for Low-Resource Cross-Lingual Named Entity
Recognition [5.030581940990434]
Cross-lingual Named Entity Recognition (NER) has recently become a research hotspot because it can alleviate the data-hungry problem for low-resource languages.
In this paper, we describe our novel dual-contrastive framework ConCNER for cross-lingual NER under the scenario of limited source-language labeled data.
arXiv Detail & Related papers (2022-04-02T07:59:13Z) - A Unified Strategy for Multilingual Grammatical Error Correction with
Pre-trained Cross-Lingual Language Model [100.67378875773495]
We propose a generic and language-independent strategy for multilingual Grammatical Error Correction.
Our approach creates diverse parallel GEC data without any language-specific operations.
It achieves the state-of-the-art results on the NLPCC 2018 Task 2 dataset (Chinese) and obtains competitive performance on Falko-Merlin (German) and RULEC-GEC (Russian)
arXiv Detail & Related papers (2022-01-26T02:10:32Z) - Improving Multilingual Translation by Representation and Gradient
Regularization [82.42760103045083]
We propose a joint approach to regularize NMT models at both representation-level and gradient-level.
Our results demonstrate that our approach is highly effective in both reducing off-target translation occurrences and improving zero-shot translation performance.
arXiv Detail & Related papers (2021-09-10T10:52:21Z) - Cross-lingual Machine Reading Comprehension with Language Branch
Knowledge Distillation [105.41167108465085]
Cross-lingual Machine Reading (CLMRC) remains a challenging problem due to the lack of large-scale datasets in low-source languages.
We propose a novel augmentation approach named Language Branch Machine Reading (LBMRC)
LBMRC trains multiple machine reading comprehension (MRC) models proficient in individual language.
We devise a multilingual distillation approach to amalgamate knowledge from multiple language branch models to a single model for all target languages.
arXiv Detail & Related papers (2020-10-27T13:12:17Z) - FILTER: An Enhanced Fusion Method for Cross-lingual Language
Understanding [85.29270319872597]
We propose an enhanced fusion method that takes cross-lingual data as input for XLM finetuning.
During inference, the model makes predictions based on the text input in the target language and its translation in the source language.
To tackle this issue, we propose an additional KL-divergence self-teaching loss for model training, based on auto-generated soft pseudo-labels for translated text in the target language.
arXiv Detail & Related papers (2020-09-10T22:42:15Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.