title: UNSUPERVISED SENTENCE EMBEDDING USING DOCUMENT STRUCTURE-BASED CONTEXT
abstract: We present a new unsupervised method for learning general-purpose sentence embeddings.
  Unlike existing methods which rely on local contexts, such as words
  inside the sentence or immediately neighboring sentences, our method selects, for
  each target sentence, influential sentences in the entire document based on a document
  structure. We identify a dependency structure of sentences using metadata
  or text styles. Furthermore, we propose a novel out-of-vocabulary word handling
  technique to model many domain-specific terms, which were mostly discarded by
  existing sentence embedding methods. We validate our model on several tasks
  showing 30% precision improvement in coreference resolution in a technical domain,
  and 7.5% accuracy increase in paraphrase detection compared to baselines.
authors: Taesung Lee Youngja Park
hIndex: Youngja Park 13