ETPC - A Paraphrase Identification Corpus Annotated with Extended Paraphrase Typology and Negation

ETPC - A Paraphrase Identification Corpus Annotated with Extended Paraphrase Typology and Negation Venelin Kovatchev, M. Ant`onia Mart´ı, Maria Salam´o Universitat de Barcelona Gran V´ıa de les Corts Catalanes, 585, 08007 Barcelona, Spain {vkovatchev, amarti, maria.salamo}@ub.edu Abstract We present the Extended Paraphrase Typology (EPT) and the Extended Typology Paraphrase Corpus (ETPC). The EPT typology addresses several practical limitations of existing paraphrase typologies: it is the first typology that copes with the non-paraphrase pairs in the paraphrase identification corpora and distinguishes between contextual and habitual paraphrase types. ETPC is the largest corpus to date annotated with atomic paraphrase types. It is the first corpus with detailed annotation of both the paraphrase and the non-paraphrase pairs and the first corpus annotated with paraphrase and negation. Both new resources contribute to better understanding the paraphrase phenomenon, and allow for studying the relationship between paraphrasing and negation. To the developers of Paraphrase Identification systems ETPC corpus offers better means for evaluation and error analysis. Furthermore, the EPT typology and ETPC corpus emphasize the relationship with other areas of NLP such as Semantic Similarity, Textual Entailment, Summarization and Simplification. Keywords: Paraphrasing, Paraphrase Typology, Paraphrase Identification 1. Introduction The task of Paraphrase Identification (PI) consists of com- paring two texts of arbitrary size in order to determine whether they have approximately the same meaning. The most common approach to PI is as a binary classification problem, in which a system learns to make correct binary predictions (paraphrase or non-paraphrase) for a given pair of texts. The task of PI is challenging from more than one point of view. From the resource point of view, defin- ing the task and preparing high quality corpora is a non- trivial problem due to the complex nature of “paraphras- ing”. From the application point of view, for a system to perform well on PI often requires a complex ML archi- tecture and/or a large set of manually engineered features. From the evaluation point of view, the classical task of PI does not offer many possibilities for detailed error analysis, which in turn limits the reusability and the improvement of PI systems. In the last few years, researchers in the field of paraphrasing have adopted the approach of decomposing the meta phe- nomenon of “textual paraphrasing” into a set of “atomic paraphrase” phenomena, which are more strictly defined and easier to work with. “Atomic paraphrases” are hier- archically organized into a typology, which provides a bet- ter means to study and understand paraphrasing. While the theoretical advantages of these approaches are clear, their practical implications have not been fully explored. The ex- isting corpora annotated with paraphrase typology are lim- ited in size, coverage and overall quality. The only corpus of sufficient size to date annotated with paraphrase typol- ogy is the corpus by Vila et al. (2015), which contains 3900 re-annotated “textual paraphrase” pairs from the MRPC corpus (Dolan et al., 2004). The use of a paraphrase typology in practical tasks has sev- eral advantages. First, “atomic paraphrases” are much …

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