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Found 6 papers, 2 papers with code
Hy-NLI: a Hybrid system for Natural Language Inference
Despite the advances in Natural Language Inference through the training of massive deep models, recent work has revealed the generalization difficulties of such models, which fail to perform on adversarial datasets with challenging linguistic phenomena.
XplaiNLI: Explainable Natural Language Inference through Visual Analytics
Advances in Natural Language Inference (NLI) have helped us understand what state-of-the-art models really learn and what their generalization power is.
GKR: Bridging the Gap between Symbolic/structural and Distributional Meaning Representations
This work focuses on an example of the third and less studied approach: it extends the Graphical Knowledge Representation (GKR) to include distributional features and proposes a division of semantic labour between the distributional and structural/symbolic features.
Composing Noun Phrase Vector Representations
First, we propose that the semantic and not the syntactic contribution of each component of a noun phrase should be considered, so that the resulting composed vectors express more of the phrase meaning.
GKR: the Graphical Knowledge Representation for semantic parsing
This paper describes the first version of an open-source semantic parser that creates graphical representations of sentences to be used for further semantic processing, e. g. for natural language inference, reasoning and semantic similarity.
Named Graphs for Semantic Representation
A position paper arguing that purely graphical representations for natural language semantics lack a fundamental degree of expressiveness, and cannot deal with even basic Boolean operations like negation or disjunction.
