Building a Knowledge Graph for American Sign Language: Enhancing ASL Models with Linguistic Insights

Core Concepts

Integrating linguistic knowledge into American Sign Language (ASL) models, specifically through a knowledge graph (ASLKG), significantly improves performance on tasks like sign recognition and semantic feature prediction, paving the way for more robust and interpretable ASL-based language technologies.

Abstract

Bibliographic Information:

Kezar, L., Munikote, N., Zeng, Z., Sehyr, Z., Caselli, N., & Thomason, J. (2024). The American Sign Language Knowledge Graph: Infusing ASL Models with Linguistic Knowledge. arXiv preprint arXiv:2411.03568.

Research Objective:

This research paper introduces the American Sign Language Knowledge Graph (ASLKG) and investigates its effectiveness in enhancing the performance and interpretability of computational models for understanding American Sign Language.

Methodology:

The researchers constructed the ASLKG by integrating data from twelve linguistic resources, encompassing ASL signs, English translations, phonological features, and semantic features. They then developed neuro-symbolic models that leverage the ASLKG for three downstream tasks: isolated sign recognition, semantic feature recognition of unseen signs, and topic classification of continuous ASL videos. These models employed techniques like knowledge graph embeddings, factor graph models, k-nearest neighbors, and multilayer perceptrons.

Key Findings:

• Integrating linguistic knowledge through the ASLKG significantly improved performance across all three tasks compared to end-to-end neural models.
• The ASLKG enabled the models to achieve 91% accuracy on isolated sign recognition, 14% accuracy on predicting semantic features of unseen signs, and 36% accuracy on classifying the topic of Youtube-ASL videos.
• The study demonstrated the effectiveness of knowledge-infused learning, where expert-annotated linguistic knowledge enhances data-driven models.

Main Conclusions:

The ASLKG is a valuable resource for developing more accurate, data-efficient, and interpretable ASL language models. Incorporating linguistic knowledge through neuro-symbolic approaches is crucial for advancing ASL technology and addressing the limitations of purely data-driven methods.

Significance:

This research significantly contributes to the field of sign language processing by providing a novel resource and demonstrating the effectiveness of knowledge-infused learning for ASL understanding. The ASLKG and the proposed neuro-symbolic methods have the potential to improve the accessibility and development of various ASL-based language technologies.

Limitations and Future Research:

The study acknowledges limitations in capturing the full range of ASL variation, including dialectal and contextual differences. Future research could focus on expanding the ASLKG to encompass greater linguistic diversity and exploring more sophisticated neuro-symbolic techniques for ASL modeling.

Stats

The ASLKG contains over 71,000 linguistic facts related to 5,802 ASL signs.
The researchers achieved 91% accuracy in isolated sign recognition using the ASLKG.
Predicting semantic features of unseen signs reached 14% accuracy with the ASLKG.
Topic classification of Youtube-ASL videos achieved 36% accuracy using the knowledge graph.

Quotes

Key Insights Distilled From

The American Sign Language Knowledge Graph: Infusing ASL Models with Linguistic Knowledge

by Lee Kezar, N... at arxiv.org 11-07-2024

https://arxiv.org/pdf/2411.03568.pdf

Deeper Inquiries

How can the ASLKG be further expanded and enriched to encompass a wider range of linguistic variations and nuances present in ASL, such as regional dialects and signing styles?

The ASLKG presents a valuable foundation for modeling ASL, but acknowledging and addressing its limitations is crucial for broader representation and inclusivity. Here's how it can be expanded:

Incorporate Dialect and Accent Data: Integrate datasets representing diverse regional dialects and accents within ASL. This could involve:

Collaborating with Deaf communities: Partner with Deaf signers from various regions to collect and annotate data reflecting their linguistic variations.
Developing dialect-specific subgraphs: Create subgraphs or layers within the ASLKG dedicated to specific dialects, capturing unique phonological, lexical, and semantic features.

Representing Signing Styles: ASL, like any language, encompasses various signing styles influenced by factors like age, gender, and social groups. The ASLKG can be enriched by:

Annotating signing styles: Include annotations for signing styles in existing and future datasets, allowing for analysis and modeling of these variations.
Developing style-aware models: Train models capable of recognizing and adapting to different signing styles, improving accuracy and user experience.

Expanding Semantic Feature Representation: Go beyond basic semantic features and incorporate:

Figurative language: Include metaphors, idioms, and other figurative language elements common in ASL.
Cultural nuances: Capture cultural references and context-specific meanings crucial for accurate understanding.

Continuous Data Collection and Curation: Establish an ongoing process for collecting and curating data from diverse ASL signers and communities. This ensures the ASLKG remains dynamic and reflective of the language's evolution.
By actively addressing these limitations, the ASLKG can evolve into a more comprehensive and inclusive resource, benefiting both research and the development of equitable ASL language technologies.

Could the reliance on English translations in the ASLKG introduce biases or limitations in representing ASL semantics, and how can these potential issues be mitigated in future iterations of the knowledge graph?

Yes, the reliance on English translations in the ASLKG can introduce biases and limitations in representing ASL semantics. Here's why and how to mitigate these issues:

Structural Differences: ASL and English have distinct grammatical structures and ways of conveying meaning. Direct translations often fail to capture the nuances and complexities of ASL, leading to an incomplete or inaccurate representation of its semantics.
Cultural Bias: English-centric translations can impose cultural biases onto ASL, potentially misinterpreting or overlooking culturally specific concepts and expressions.
Limited Semantic Scope: Relying solely on translations restricts the semantic scope of the ASLKG to concepts readily translatable into English, neglecting aspects of ASL semantics that might not have direct English equivalents.
Mitigating Translation Bias:

Prioritize ASL-Centric Definitions: Instead of relying solely on English translations, prioritize developing ASL-centric definitions and descriptions of signs and their semantic relations. This involves:

Engaging Deaf Linguists: Collaborate closely with Deaf linguists and language experts to define ASL concepts within the context of Deaf culture and language structure.
Utilizing ASL Corpora: Analyze large ASL corpora to identify semantic patterns and relationships directly from ASL usage, reducing dependence on English as an intermediary.

Incorporate Multilingual Perspectives: While reducing reliance on English is key, incorporating translations from multiple languages can offer a broader perspective on ASL semantics. This can help identify potential biases stemming from a single language lens.
Develop Cross-Lingual Embeddings: Explore techniques like cross-lingual embeddings that map ASL and English words into a shared semantic space. This can help identify semantic similarities and differences more accurately while acknowledging the languages' distinct structures.
By actively addressing the limitations of relying solely on English translations, future iterations of the ASLKG can move towards a more accurate and culturally sensitive representation of ASL semantics.

What are the ethical considerations surrounding the development and deployment of ASL language technologies, particularly concerning data privacy, consent, and the potential impact on Deaf communities?

Developing and deploying ASL language technologies demands careful consideration of ethical implications, particularly regarding data privacy, consent, and potential impact on Deaf communities. Here are key considerations:

Data Privacy and Consent:

Informed Consent: Obtaining truly informed consent from Deaf signers for data collection is paramount. This involves clear and accessible explanations of:

Data usage: How the data will be used, including potential commercial applications.
Data ownership: Who owns the data and controls its access and distribution.
Data security: Measures taken to protect data from unauthorized access or misuse.

Anonymization and De-Identification: Implement robust anonymization techniques to protect the identities of signers in datasets, especially when dealing with sensitive information.

Impact on Deaf Communities:

Community Engagement: Involve Deaf communities throughout the entire technology development lifecycle, from conception and design to testing and deployment. This ensures that the technology meets their needs and addresses their concerns.
Accessibility and Affordability: Strive to make ASL language technologies accessible and affordable for all members of the Deaf community, regardless of socioeconomic background or geographic location.
Job Displacement Concerns: Acknowledge and address potential concerns about job displacement within the Deaf community, such as sign language interpreters. Explore ways to integrate these technologies collaboratively rather than replacing human roles entirely.

Bias and Discrimination:

Algorithmic Bias: Be vigilant about potential biases in algorithms trained on limited or skewed datasets. This can lead to inaccurate or discriminatory outcomes for certain groups within the Deaf community.
Cultural Sensitivity: Design technologies that are culturally sensitive and respectful of Deaf culture and language norms. Avoid imposing hearing cultural perspectives or values onto ASL technologies.
Ethical Deployment:

Transparency and Accountability: Be transparent about the limitations and potential biases of ASL language technologies. Establish clear mechanisms for feedback and accountability to address any unintended negative consequences.
Equitable Access and Benefit: Ensure that the benefits of ASL language technologies are distributed equitably within the Deaf community, bridging the communication gap and promoting social inclusion.
By prioritizing ethical considerations throughout the development and deployment process, we can harness the potential of ASL language technologies to empower Deaf communities while mitigating potential harms.