Design and realization of a modular architecture for textual entailment
By Sebastian Padó, Tae-Gil Noh, Asher Stern, Rui Wang, Roberto Zanoli. 2013
40 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Mohit Bansal
- Samuel R. Bowman
- Omer Levy
- Luke S. Zettlemoyer
- Christopher D. Manning
- Yejin Choi
- Yoav Goldberg
- Noah A. Smith
In Natural Language Processing
- Samuel R. Bowman
- Mohit Bansal
- Christopher D. Manning
- Omer Levy
- Ellie Pavlick
- Luke S. Zettlemoyer
- Yonatan Belinkov
- Yejin Choi
Citations in the corpus
- A large annotated corpus for learning natural language inference (2015, background): “The first class of models is from the Excitement Open Platform (EOP, Pad´o et al. 2014; Magnini et al. 2014)—an open source platform for RTE research.”
- Most “babies” are “little” and most “problems” are “huge”: Compositional Entailment in Adjective-Nouns (2016, background): “, 2014), but also in statistical RTE systems (Stern and Dagan, 2012; Padó et al., 2014).”
- The Excitement Open Platform for Textual Inferences (2014, background): “The EOP architecture (Padó et al., 2014) is based on the concept of modularization with pluggable and replaceable components to enable extension and customization.”