Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation (2003.13830v1)

Abstract: Prior work on Sign Language Translation has shown that having a mid-level sign gloss representation (effectively recognizing the individual signs) improves the translation performance drastically. In fact, the current state-of-the-art in translation requires gloss level tokenization in order to work. We introduce a novel transformer based architecture that jointly learns Continuous Sign Language Recognition and Translation while being trainable in an end-to-end manner. This is achieved by using a Connectionist Temporal Classification (CTC) loss to bind the recognition and translation problems into a single unified architecture. This joint approach does not require any ground-truth timing information, simultaneously solving two co-dependant sequence-to-sequence learning problems and leads to significant performance gains. We evaluate the recognition and translation performances of our approaches on the challenging RWTH-PHOENIX-Weather-2014T (PHOENIX14T) dataset. We report state-of-the-art sign language recognition and translation results achieved by our Sign Language Transformers. Our translation networks outperform both sign video to spoken language and gloss to spoken language translation models, in some cases more than doubling the performance (9.58 vs. 21.80 BLEU-4 Score). We also share new baseline translation results using transformer networks for several other text-to-text sign language translation tasks.

An Academic Analysis of "Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation"

The paper "Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation" presents a sophisticated approach to addressing the dual challenges of Sign Language Recognition (SLR) and Sign Language Translation (SLT) through a joint learning paradigm using transformer architectures. This research represents a significant stride in overcoming the traditionally separate treatments of recognition and translation in computational sign language processing.

Core Contributions

Key to the paper's contributions is the introduction of a novel transformer-based architecture designed to simultaneously tackle the interconnected tasks of Continuous Sign Language Recognition (CSLR) and SLT. This is achieved by employing a Connectionist Temporal Classification (CTC) loss to bridge spatial-temporal sign language data with spoken language translations, without the dependency on ground-truth alignment information. The architecture proposed by the authors integrates two primary components: the Sign Language Recognition Transformer (SLRT) and the Sign Language Translation Transformer (SLTT). This configuration effectively utilizes shared learning objectives and improves task performance by harnessing the mutual dependencies of SLR and SLT tasks.

Experimental Evaluation and Results

Empirical assessments conducted on the RWTH-PHOENIX-Weather-2014T dataset exhibit the transformative potential of the model. Notably, the authors report a performance that in some cases more than doubles previous results in translation accuracy, as measured by BLEU-4 scores. The joint learning approach outperforms previous state-of-the-art methods by generating spoken language translations directly from continuous sign videos and surpasses the intermediate representation bottlenecks of gloss-based approaches.

The experimental results are significant, demonstrating a 21.80 BLEU-4 score, juxtaposed with a prior best of 9.58 from end-to-end translation models, rivalling even the gloss-to-text translation scores that have been used as a benchmark for system comparison. In a system configuration that manages both recognition and translation, the authors maintain a commendable Word Error Rate (WER) of 24.49%, showcasing effective handling of the SLR task.

Theoretical Implications

This paper underscores the capacity of transformers to manage complex multi-task and sequence-to-sequence problems, especially in domains requiring nuanced handling of spatial-temporal data, such as sign languages. The elimination of intermediary gloss tokenization not only streamlines the workflow but also reflects a theoretical evolution in SLT models. The method advocates for a more comprehensive understanding of sign language through model architectures that do not accentuate one facet of translation (such as gloss recognition) over the holistic translation process from raw visual data to meaningful spoken language.

Practical Implications and Future Directions

Practically, the success of this model enhances the feasibility of deploying automated sign language translation systems in real-world applications, presenting vast opportunities for improving accessibility for the deaf and hard-of-hearing communities. The potential scalability of this approach, given the advancement in computational resources and model efficiencies, suggests a promising horizon for robust SLT applications in varied and unconstrained environments.

Looking forward, future work could expand on incorporating finer articulatory elements in sign languages, such as facial expressions and nuanced hand morphologies, into the joint learning paradigm. Methodologies to further optimize model training and inference times and integration with other linguistic modalities may also be pivotal research directions.

In summary, the paper delivers a compelling argument and substantiates the shift toward joint modeling in sign language processing, opening avenues for more integrated and accessible communication technologies.