Improving Multilingual Semantic Textual Similarity with Shared Sentence Encoder for Low-resource Languages

Understand

Measuring the semantic similarity between two sentences (or Semantic Textual Similarity - STS) is fundamental in many NLP applications.

• Despite the remarkable results in supervised settings with adequate labeling, little attention has been paid to this task in low-resource languages with insufficient labeling.
• Existing approaches mostly leverage machine translation techniques to translate sentences into rich-resource language.
• These approaches either beget language biases, or be impractical in industrial applications where spoken language scenario is more often and rigorous efficiency is required.