Gloss2Text: Sign Language Gloss translation using LLMs and Semantically Aware Label Smoothing

• URL: http://arxiv.org/abs/2407.01394v2
• Date: Fri, 12 Jul 2024 14:44:33 GMT
• Title: Gloss2Text: Sign Language Gloss translation using LLMs and Semantically Aware Label Smoothing
• Authors: Pooya Fayyazsanavi, Antonios Anastasopoulos, Jana Košecká,
• Abstract summary: We propose several advances by leveraging pre-trained large language models (LLMs), data augmentation, and novel label-smoothing loss function. Our approach surpasses state-of-the-art performance in em Gloss2Text translation.
• Score: 21.183453511034767
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sign language translation from video to spoken text presents unique challenges owing to the distinct grammar, expression nuances, and high variation of visual appearance across different speakers and contexts. The intermediate gloss annotations of videos aim to guide the translation process. In our work, we focus on {\em Gloss2Text} translation stage and propose several advances by leveraging pre-trained large language models (LLMs), data augmentation, and novel label-smoothing loss function exploiting gloss translation ambiguities improving significantly the performance of state-of-the-art approaches. Through extensive experiments and ablation studies on the PHOENIX Weather 2014T dataset, our approach surpasses state-of-the-art performance in {\em Gloss2Text} translation, indicating its efficacy in addressing sign language translation and suggesting promising avenues for future research and development.

Related papers

• DiffSLT: Enhancing Diversity in Sign Language Translation via Diffusion Model [9.452839238264286]
  We propose DiffSLT, a novel gloss-free sign language translation framework. DiffSLT transforms random noise into the target latent representation conditioned on the visual features of input video. We also introduce DiffSLT-P, a DiffSLT variant that conditions on pseudo-glosses visual features, providing key textual guidance and reducing the modality gap.
  arXiv  Detail & Related papers  (2024-11-26T09:26:36Z)
• American Sign Language Video to Text Translation [0.0]
  Sign language to text is a crucial technology that can break down communication barriers for individuals with hearing difficulties. We evaluate models using BLEU and rBLEU metrics to ensure translation quality.
  arXiv  Detail & Related papers  (2024-02-11T17:46:33Z)
• VK-G2T: Vision and Context Knowledge enhanced Gloss2Text [60.57628465740138]
  Existing sign language translation methods follow a two-stage pipeline: first converting the sign language video to a gloss sequence (i.e. Sign2Gloss) and then translating the generated gloss sequence into a spoken language sentence (i.e. Gloss2Text) We propose a vision and context knowledge enhanced Gloss2Text model, named VK-G2T, which leverages the visual content of the sign language video to learn the properties of the target sentence and exploit the context knowledge to facilitate the adaptive translation of gloss words.
  arXiv  Detail & Related papers  (2023-12-15T21:09:34Z)
• Unified Language-Vision Pretraining in LLM with Dynamic Discrete Visual Tokenization [52.935150075484074]
  We introduce a well-designed visual tokenizer to translate the non-linguistic image into a sequence of discrete tokens like a foreign language. The resulting visual tokens encompass high-level semantics worthy of a word and also support dynamic sequence length varying from the image. This unification empowers LaVIT to serve as an impressive generalist interface to understand and generate multi-modal content simultaneously.
  arXiv  Detail & Related papers  (2023-09-09T03:01:38Z)
• ViLTA: Enhancing Vision-Language Pre-training through Textual Augmentation [35.05755930636518]
  We propose ViLTA, comprising of two components to further facilitate the model to learn fine-grained representations among image-text pairs. For Masked Language Modeling (MLM), we propose a cross-distillation method to generate soft labels to enhance the robustness of model. For Image-Text Matching (ITM), we leverage the current language encoder to synthesize hard negatives based on the context of language input.
  arXiv  Detail & Related papers  (2023-08-31T12:46:36Z)
• 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)
• Cross-modality Data Augmentation for End-to-End Sign Language Translation [66.46877279084083]
  End-to-end sign language translation (SLT) aims to convert sign language videos into spoken language texts directly without intermediate representations. It has been a challenging task due to the modality gap between sign videos and texts and the data scarcity of labeled data. We propose a novel Cross-modality Data Augmentation (XmDA) framework to transfer the powerful gloss-to-text translation capabilities to end-to-end sign language translation.
  arXiv  Detail & Related papers  (2023-05-18T16:34:18Z)
• SimulSLT: End-to-End Simultaneous Sign Language Translation [55.54237194555432]
  Existing sign language translation methods need to read all the videos before starting the translation. We propose SimulSLT, the first end-to-end simultaneous sign language translation model. SimulSLT achieves BLEU scores that exceed the latest end-to-end non-simultaneous sign language translation model.
  arXiv  Detail & Related papers  (2021-12-08T11:04:52Z)
• Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation [59.38247587308604]
  We introduce a novel transformer based architecture that jointly learns Continuous Sign Language Recognition and Translation. We evaluate the recognition and translation performances of our approaches on the challenging RWTH-PHOENIX-Weather-2014T dataset. Our translation networks outperform both sign video to spoken language and gloss to spoken language translation models.
  arXiv  Detail & Related papers  (2020-03-30T21:35:09Z)

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.