Paraphrase and Textual Entailment Generation

One particular information can be conveyed by many different sentences. This variety concerns the choice of vocabulary and style as well as the level of detail (from laconism or succinctness to total verbosity). Although verbosity in written texts is considered bad style, generated verbosity can help natural language processing (NLP) systems to fill in the implicit knowledge. The paper presents a rule-based system for paraphrasing and textual entailment generation in Czech. The inner representation of the input text is transformed syntactically or lexically in order to produce two type of new sentences: paraphrases (sentences with similar meaning) and entailments (sentences that humans will infer from the input text). The transformations make use of several language resources as well as a natural language generation (NLG) subsystem. The paraphrases and entailments are annotated by one or more annotators. So far, we annotated 3,321 paraphrases and textual entailments, from which 1,563 were judged correct (47.1 %), 1,238 (37.3 %) were judged incorrect entailments, and 520 (15.6 %) were judged non-sense. Paraphrasing and textual entailment can be put into effect in chatbots, text summarization or question answering systems. The results can encourage applicationdriven creation of new language resources or improvement of the current ones.