Knowledge-Based Textual Inference via Parse-Tree Transformations
By Roy Bar-Haim, Ido Dagan, Jonathan Berant. 2015
11 citations recorded by the source corpus.
Textual inference is an important component in many applications for understanding natural language. Classical approaches to textual inference rely on logical representations for meaning, which may be regarded as "external" to the natural language itself. However, practical applications usually adopt shallower lexical or lexical-syntactic representations, which correspond closely to language structure. In many cases, such approaches lack a principled meaning representation and inference framework. We describe an inference formalism that operates directly on language-based structures, particularly syntactic parse trees. New trees are generated by applying inference rules, which provide a unified representation for varying types of inferences. We use manual and automatic methods to generate these rules, which cover generic linguistic structures as well as specific lexical-based inferences. We also present a novel packed data-structure and a corresponding inference algorithm that allows efficient implementation of this formalism. We proved the correctness of the new algorithm and established its efficiency analytically and empirically. The utility of our approach was illustrated on two tasks: unsupervised relation extraction from a large corpus, and the Recognizing Textual Entailment (RTE) benchmarks.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Ashish Sabharwal
- Tushar Khot
- Daniel Khashabi
- Peter Clark
- Dan Roth
- Hannaneh Hajishirzi
- Luke S. Zettlemoyer
- Yejin Choi
Citations in the corpus
- Diversify Your Datasets: Analyzing Generalization via Controlled Variance in Adversarial Datasets (2019, background): “Pre-neural systems often included explicit components, such as engineered features or syntax-based transformations (e.g. Stern and Dagan, 2012; Stern et al., 2012; Bar-Haim et al., 2015), to address particular inference types such as syntactic, lexical, and logical inferences.”
- Question Answering as Global Reasoning Over Semantic Abstractions (2018, background): “Bar-Haim, Dagan, and Berant (2015) and Stern et al. (2012) search over a space of a pre-defined set of text transformations (e.g., coreference substitutions, passive to active).”