Natural Language Inference with Monotonicity
By Hai Hu, Qi Chen, Larry Moss. 2019
15 citations recorded by the source corpus.
This paper describes a working system which performs natural language inference using polarity-marked parse trees. The system handles all of the instances of monotonicity inference in the FraCaS data set. Except for the initial parse, it is entirely deterministic. It handles multi-premise arguments, and the kind of inference performed is essentially “logical”, but it goes beyond what is representable in first-order logic. In any case, the system works on surface forms rather than on representations of any kind.
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
- Kyle Richardson
- Ashish Sabharwal
- Andreas Vlachos
- Sebastian Riedel
- Mohit Bansal
- Peter Clark
- Hai Hu
- Larry Moss
In Natural Language Processing
- Kyle Richardson
- Ashish Sabharwal
- Andreas Vlachos
- Sebastian Riedel
- Hai Hu
- Larry Moss
- Sandra Kübler
- Peter Clark
Citations in the corpus
- ProoFVer: Natural Logic Theorem Proving for Fact Verification (2021, background): “Natural logic is a prominent logic based paradigm in textual entailment (Abzianidze, 2017a; Hu et al., 2019; Feng et al., 2020).”
- Probing Natural Language Inference Models through Semantic Fragments (2019, methodology): “To construct these datasets, we build on recent work on automatic polarity projection (Hu and Moss 2018; Hu, Chen, and Moss 2019; Hu et al. 2019).”
- MonaLog: a Lightweight System for Natural Language Inference Based on Monotonicity (2019, background): “This extension is addressed in more detail in Hu et al. (2019).”