Lexicosyntactic Inference in Neural Models
By Aaron Steven White, Rachel Rudinger, Kyle Rawlins, Benjamin Van Durme. 2018
39 citations recorded by the source corpus.
We investigate neural models’ ability to capture lexicosyntactic inferences: inferences triggered by the interaction of lexical and syntactic information. We take the task of event factuality prediction as a case study and build a factuality judgment dataset for all English clause-embedding verbs in various syntactic contexts. We use this dataset, which we make publicly available, to probe the behavior of current state-of-the-art neural systems, showing that these systems make certain systematic errors that are clearly visible through the lens of factuality prediction.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, 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
- Douwe Kiela
- Samuel R. Bowman
- Luke S. Zettlemoyer
- Yejin Choi
- Yonatan Belinkov
- Sebastian Riedel
- Noah A. Smith
In Computer Vision and Pattern Recognition
- Douwe Kiela
- Mohit Bansal
- Robin Jia
- Sebastian Riedel
- Tristan Thrush
- Adina Williams
- Samuel R. Bowman
- Pontus Stenetorp
In Natural Language Processing
- Mohit Bansal
- Douwe Kiela
- Samuel R. Bowman
- Robin Jia
- Adina Williams
- Yonatan Belinkov
- Yejin Choi
- Sebastian Riedel
Citations in the corpus
- Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference (2019, background): “Several past works (Pavlick and Callison-Burch, 2016; Rudinger et al., 2018; White et al., 2018; White and Rawlins, 2018) have studied such behavior for verbs (e.g., He knows it is raining entails It is raining , while He believes it is raining does not).”
- Dynabench: Rethinking Benchmarking in NLP (2021, background): “…et al., 2018; May et al., 2019; Nangia et al., 2020) and/or linguistic competencies (Geiger et al., 2018; Naik et al., 2018; Glockner et al., 2018; White et al., 2018; Warstadt et al., 2019; Gauthier et al., 2020; Hossain et al., 2020; Jeretic et al., 2020; Lewis et al., 2020; Saha et al.,…”
- Automatically Neutralizing Subjective Bias in Text (2019, methodology): “This task shares many properties with tasks like detecting framing or epistemological bias (Recasens, DanescuNiculescu-Mizil, and Jurafsky 2013), or veridicality assessment/factuality prediction (Saurı́ and Pustejovsky 2009; Marneffe, Manning, and Potts 2012; Rudinger, White, and Van Durme 2018; Whi”