Improving Sign Language Translation Through a Novel Two-Step Approach: Gloss Selection and Gloss Reordering

Core Concepts

A novel two-step approach, Select and Reorder (S&R), is proposed to address the challenges of sign language translation by disentangling the tasks of vocabulary selection and word order reordering.

Abstract

The paper introduces a novel approach called Select and Reorder (S&R) for Text to Gloss (T2G) translation in sign language production. The approach breaks down the translation task into two sub-tasks: Gloss Selection (GS) and Gloss Reordering (GR). GS focuses on predicting the corresponding gloss for each word in the spoken language sentence, producing a Spoken Language Order (SPO) gloss sequence. This is achieved by leveraging the lexical overlap between the source and target languages to establish a pseudo-alignment between words and glosses. GR then changes the order of the gloss sequence from SPO to Sign Language Order (SIO). Two approaches are explored for GR: a statistical method using a Top-Down Bracketing Transduction Grammar (BTG) and a deep learning transformer-based model. Both GS and GR models use Non-AutoRegressive (NAR) decoding, which reduces computational requirements and accelerates inference speed. The outputs of the GS and GR models are then combined to obtain the final translation. The S&R approach is evaluated on the mDGS and PHOENIX14T datasets, achieving state-of-the-art BLEU and ROUGE scores. Notably, the GS model alone outperforms previous methods, demonstrating the effectiveness of the lexical alignment technique. The reordering step further improves the translation quality, though the statistical approach is found to be more effective than the learned method given the limited training data. The paper highlights the advantages of the S&R approach, including significant improvements in inference speed compared to a traditional transformer-based model.

Stats

The mDGS dataset contains 330 deaf participants performing free-form signing, with a source vocabulary of 18,457 words. The PHOENIX14T dataset has a lexical overlap of 33% between the source and target sequences.

Quotes

"Sign languages, often categorised as low-resource languages, face significant challenges in achieving accurate translation due to the scarcity of parallel annotated datasets." "By first formatting the gloss tokens with lemmatization we find that datasets such as Meine DGS Annotated (mDGS) Konrad et al. (2020) and RWTH-PHOENIX-Weather-2014T (PHOENIX14T) (Camgoz et al., 2018) have a lexical overlap of 35% and 33%, respectively."

Key Insights Distilled From

Select and Reorder: A Novel Approach for Neural Sign Language Production

by Harry Walsh,... at arxiv.org 04-18-2024

https://arxiv.org/pdf/2404.11532.pdf

Deeper Inquiries

How could the proposed S&R approach be extended to handle more complex sign language structures, such as non-manual features and spatial-temporal information?

The Select and Reorder (S&R) approach could be extended to handle more complex sign language structures by incorporating additional components into the model architecture. To address non-manual features, which include facial expressions, body movements, and other non-manual elements crucial for sign language communication, the model could be enhanced with multi-modal inputs. By integrating video or image data alongside the spoken language input, the model can learn to generate sign language sequences that capture these non-manual features. Spatial-temporal information, such as the movement of hands and body in sign language, can be encoded using techniques like 3D pose estimation or motion capture data. By incorporating these elements into the training process, the model can learn to produce more accurate and expressive sign language translations.

What other techniques could be explored to further improve the reordering step, especially in the case of limited training data?

In the case of limited training data for the reordering step, several techniques can be explored to improve the performance of the model. One approach is to leverage transfer learning from related tasks or languages with more data. By pre-training the model on a larger dataset or a similar task, the model can learn general language patterns and improve its reordering capabilities. Data augmentation techniques, such as back-translation or synthetic data generation, can also be employed to increase the diversity of the training data and improve the model's robustness. Additionally, semi-supervised learning methods, where the model learns from both labeled and unlabeled data, can be beneficial in scenarios with limited annotated examples. By combining these techniques, the model can enhance its reordering abilities even with restricted training data.

Could the S&R framework be applied to other low-resource language translation tasks beyond sign language, and what would be the key considerations in adapting the approach?

Yes, the Select and Reorder (S&R) framework can be applied to other low-resource language translation tasks beyond sign language. The key considerations in adapting the approach to different languages include: Language Specificity: Understanding the unique grammatical rules, syntax, and vocabulary of the target language is crucial for effective translation. Adapting the S&R approach to different languages would require language-specific data preprocessing and model tuning. Data Availability: Ensuring the availability of parallel annotated datasets for training is essential. In low-resource language scenarios, techniques like data augmentation, transfer learning, and semi-supervised learning can help mitigate data scarcity issues. Model Architecture: Tailoring the model architecture to the linguistic characteristics of the target language is important. For languages with complex structures, like agglutinative or tonal languages, the model may need additional components or modifications to capture these nuances effectively. Evaluation Metrics: Choosing appropriate evaluation metrics that align with the linguistic properties of the target language is essential. BLEU scores, Rouge scores, and other standard metrics may need to be adapted or supplemented with language-specific evaluation criteria. By considering these factors and customizing the S&R framework to the specific requirements of the target language, it can be successfully applied to a wide range of low-resource language translation tasks, facilitating more effective and accurate translations.