Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing

81 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Inferring textual entailment with a probabilistically sound calculus* (2009, methodology): “Tables 2 and 3 show the performance of the probabilistic calculus and logistic regression on the datasets from the second and third PASCAL Challenge on Recognizing Textual Entailment (see Bar-Haim et al. 2006; Giampiccolo et al. 2007).”
Enhanced LSTM for Natural Language Inference (2016, methodology): “Early work on natural language inference has been performed on rather small datasets with more conventional methods (refer to MacCartney (2009) for a good literature survey), which includes a large bulk of work on recognizing textual entailment, such as (Dagan et al., 2005; Iftene and Balahur-Dobres”
Generating Phrasal and Sentential Paraphrases: A Survey of Data-Driven Methods (2010, background): “…noticeable attention in the research community and annual community-wide evaluations of entailment systems have been held in the form of the Recognizing Textual Entailment (RTE) Challenges (Dagan, Glickman, and Magnini 2006; Bar-Haim et al. 2007; Sekine et al. 2007; Giampiccolo et al. 2008).”
Enhancing and Combining Sequential and Tree LSTM for Natural Language Inference (2016, methodology): “performed on rather small datasets with more conventional methods (refer to MacCartney (2009) for a good literature survey), which includes a large bulk of early work on recognizing textual entailment, such as (Dagan et al., 2005; Iftene and Balahur-Dobrescu, 2007), among others.”