  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. In this work, we propose a multilingual framework to tackle the STS
task in a low-resource language e.g. Spanish, Arabic , Indonesian and Thai, by
utilizing the rich annotation data in a rich resource language, e.g. English.
Our approach is extended from a basic monolingual STS framework to a shared
multilingual encoder pretrained with translation task to incorporate
rich-resource language data. By exploiting the nature of a shared multilingual
encoder, one sentence can have multiple representations for different target
translation language, which are used in an ensemble model to improve similarity
evaluation. We demonstrate the superiority of our method over other state of
the art approaches on SemEval STS task by its significant improvement on non-MT
method, as well as an online industrial product where MT method fails to beat
baseline while our approach still has consistently improvements.
