Large Language Models Show Promise as Efficient Sign Language Translators

Основные понятия

Large language models (LLMs) trained on extensive multilingual text corpora can be effectively harnessed to handle the challenging task of sign language translation (SLT) by regularizing sign videos into a language-like representation.

Аннотация

The paper explores leveraging the impressive translation capabilities of large language models (LLMs) to tackle the challenging task of sign language translation (SLT). SLT aims to translate sign videos into spoken language, but it is a difficult task that requires cross-modal understanding of visual and linguistic cues, exacerbated by the limited availability of paired sign-text data.

The key insights are:

1. LLMs trained on large multilingual text corpora possess rich semantic understanding and powerful linguistic abilities, including strong translation capabilities across languages.
2. To effectively harness LLMs for SLT, the authors propose the SignLLM framework, which regularizes the input sign videos into a language-like representation that is compatible and readable for off-the-shelf, frozen LLMs.
3. SignLLM comprises two main modules:
   • Vector-Quantized Visual Sign (VQ-Sign) module: Converts the sign video into a sequence of discrete character-level sign tokens, aligning the sign representations with the discrete characteristics of language.
   • Codebook Reconstruction and Alignment (CRA) module: Transforms the character-level sign tokens into word-level sign tokens, imparting a hierarchical structure akin to language, and further aligns the sign token space with the LLM's text token space.
4. By producing these language-like sign representations, the authors are able to leverage a frozen off-the-shelf LLM to achieve state-of-the-art gloss-free SLT performance on two benchmark datasets.

Статистика

"Sign languages, which are visual signals expressed through hand, body, and facial movements, serve as the primary means of communication within the hearing-impaired community."
"LLMs have also demonstrated an impressive capability to translate across multiple languages, even showing a strong potential for translating languages with limited data."
"Inspired by the impressive translation capabilities of LLMs, we aim to harness off-the-shelf LLMs to handle the challenging SLT task."

Цитаты

"Inspired by the strong translation capabilities of large language models (LLMs) that are trained on extensive multilingual text corpora, we aim to harness off-the-shelf LLMs to handle SLT."
"Notably, this is not a straightforward task because directly encoding features from sign videos with a pre-trained feature extractor will result in a large gap between the sign video features and text tokens, making it difficult for off-the-shelf LLMs to understand them."
"We achieve state-of-the-art gloss-free results on two widely-used SLT benchmarks."

Ключевые выводы из

LLMs are Good Sign Language Translators

by Jia Gong,Lin... в arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00925.pdf

Дополнительные вопросы

How can the SignLLM framework be extended to handle sign languages with more diverse vocabularies and grammatical structures?

To extend the SignLLM framework for sign languages with more diverse vocabularies and grammatical structures, several approaches can be considered:

Adapting the Codebook: The character-level and word-level codebooks in the SignLLM framework can be expanded to accommodate a larger vocabulary. By increasing the number of tokens in the codebooks, the model can better represent the diverse vocabulary of different sign languages.

Language-specific Training: Training the SignLLM on a more extensive dataset that includes a wider range of signs, vocabulary, and grammatical structures specific to the target sign language can improve its performance. This can help the model learn the nuances and intricacies of different sign languages.

Fine-tuning with Language-specific Data: After pre-training on a general dataset, fine-tuning the SignLLM with language-specific data can help it adapt to the unique characteristics of a particular sign language. This process can enhance the model's ability to handle diverse vocabularies and grammatical structures.

Incorporating Multimodal Data: Including additional modalities such as facial expressions, body movements, and contextual information in the training data can provide more context for the model to understand the nuances of different sign languages. This multimodal approach can improve the model's performance on languages with diverse structures.

Hierarchical Representation Learning: Implementing hierarchical representation learning techniques can help the model capture the complex grammatical structures of different sign languages. By encoding information at different levels of abstraction, the model can better understand and generate diverse linguistic patterns.

By incorporating these strategies, the SignLLM framework can be extended to effectively handle sign languages with more diverse vocabularies and grammatical structures.

What are the potential limitations of relying on off-the-shelf LLMs for sign language translation, and how can these be addressed?

While off-the-shelf LLMs offer powerful capabilities for translation tasks, there are some limitations when applying them to sign language translation:

Limited Sign Language Understanding: Off-the-shelf LLMs may lack specific knowledge and understanding of sign languages, leading to challenges in accurately translating sign videos into spoken language. This can result in errors and inaccuracies in the translation process.

Vocabulary and Grammar Mismatches: Sign languages have unique vocabularies and grammatical structures that may not align perfectly with spoken languages. Off-the-shelf LLMs trained on text data may struggle to capture these nuances, leading to translation errors.

Lack of Contextual Understanding: Sign language relies heavily on visual and spatial cues, which may not be fully captured by off-the-shelf LLMs designed for text-based tasks. This limitation can impact the model's ability to understand and translate sign language effectively.

Data Bias and Representation: Off-the-shelf LLMs are trained on large text corpora, which may not adequately represent the diversity and complexity of sign languages. This can result in biases and inaccuracies in the translation output.

To address these limitations, several strategies can be implemented:

Fine-tuning on Sign Language Data: Fine-tuning the off-the-shelf LLM on sign language data can help the model adapt to the unique characteristics of sign languages. This process can improve the model's understanding and translation accuracy for sign language tasks.

Multimodal Training: Incorporating multimodal training data that includes both sign videos and corresponding text translations can enhance the model's ability to learn the relationships between visual and linguistic elements in sign languages. This approach can improve the model's performance on sign language translation tasks.

Domain-specific Preprocessing: Preprocessing the sign language data to emphasize key visual features and linguistic cues specific to sign languages can help the model focus on relevant information during training. This can improve the model's ability to capture the nuances of sign language communication.

By addressing these limitations through targeted training strategies and data preprocessing techniques, the performance of off-the-shelf LLMs for sign language translation can be enhanced.

What other modalities or auxiliary information, beyond just the sign video, could be leveraged to further improve the performance of sign language translation systems?

In addition to sign videos, leveraging other modalities and auxiliary information can enhance the performance of sign language translation systems:

Facial Expressions and Body Movements: Facial expressions and body movements play a crucial role in sign language communication. Incorporating information about facial expressions, gestures, and body language into the translation system can provide valuable context for understanding the emotional and expressive aspects of sign language.

Contextual Information: Contextual information, such as the topic of conversation, the speaker's identity, and the setting in which the sign language communication takes place, can help the translation system generate more accurate and contextually relevant translations. This information can guide the model in producing more coherent and meaningful translations.

Gloss Annotations: Gloss annotations provide linguistic information about the signs used in sign language videos. By incorporating gloss annotations into the training data, the translation system can learn the mappings between signs and their corresponding spoken language representations, improving translation accuracy.

User Feedback and Corrections: Integrating user feedback and corrections into the training process can help the model learn from its mistakes and improve over time. By allowing users to provide feedback on the translations and corrections to the output, the system can iteratively refine its performance.

Interactive Interfaces: Interactive interfaces that allow users to interact with the translation system in real-time can facilitate more dynamic and responsive translations. Features such as real-time feedback, correction suggestions, and adaptive learning based on user interactions can enhance the user experience and translation quality.

By incorporating these additional modalities and auxiliary information, sign language translation systems can become more robust, accurate, and user-friendly, ultimately improving communication accessibility for the deaf and hard of hearing community.