Generating Textual Entailment Using Residual LSTMs

• Published in: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data Vol. 10565; pp. 263 - 272
• Main Authors: Guo, Maosheng, Zhang, Yu, Zhao, Dezhi, Liu, Ting
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2017; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Artificial intelligence; Generating textual entailment; Natural language generation; Natural language processing

Generating textual entailment (GTE) is a recently proposed task to study how to infer a sentence from a given premise. Current sequence-to-sequence GTE models are prone to produce invalid sentences when facing with complex enough premises. Moreover, the lack of appropriate evaluation criteria hinder...