This paper introduces a sentence to vector encoding framework suitable for advanced natural language processing. Our latent representation is shown to encode sentences with common semantic information with similar vector representations. The vector representation is extracted from an encoderdecoder model which is trained on sentence paraphrase pairs. We demonstrate the application of the sentence representations for two different tasks -sentence paraphrasing and paragraph summarization, making it attractive for commonly used recurrent frameworks that process text. Experimental results help gain insight how vector representations are suitable for advanced language embedding.
REF encodes sentences with common semantic information to similar vector representations.
Semantic sentence embeddings for paraphrasing and text summarization
2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
Computer Science