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Area of Science:

  • Computational Linguistics
  • Natural Language Processing
  • Sign Language Linguistics

Background:

  • Developing machine translation for sign languages is challenging due to data scarcity.
  • Mexican Sign Language (MSL) lacks comprehensive digital resources for computational linguistics tasks.
  • Bridging the gap between spoken and signed languages requires specialized datasets and methodologies.

Purpose of the Study:

  • To create and share a valuable dataset of Spanish (SPA) to Mexican Sign Language (MSL) glosses.
  • To establish a benchmark corpus for evaluating neural machine translation (NMT) systems for sign languages.
  • To facilitate research in low-resource language translation and sign language technology.

Main Methods:

  • Compilation of a 3000-sentence pair SPA-to-MSL corpus, guided by MSL grammar resources and expert interpreters.
  • Linguistic alignment of the Spanish sentences to reflect MSL structure for accurate interpretation.
  • Implementation and evaluation of two NMT models (Helsinki-NLP and BARTO transformer) using a transfer learning approach.

Main Results:

  • The SPA-to-MSL dataset demonstrated high performance when used with NMT models, achieving BLEU scores of 91.13 and 94.23.
  • These results align with state-of-the-art NMT performance for general language pairs.
  • Professional MSL interpreters validated the translation effectiveness at 94% accuracy.

Conclusions:

  • The developed SPA-to-MSL dataset is a significant resource for advancing sign language translation research.
  • Transfer learning is effective for NMT in low-resource scenarios, as shown by the high BLEU scores.
  • The dataset supports the development of more accurate and accessible MSL interpretation technologies.