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Found 12 papers, 6 papers with code
TextGraphs 2022 Shared Task on Natural Language Premise Selection
In this summary paper, we present the results of the 1st edition of the NLPS task, providing a description of the evaluation data, and the participating systems.
To be or not to be an Integer? Encoding Variables for Mathematical Text
The application of Natural Language Inference (NLI) methods over large textual corpora can facilitate scientific discovery, reducing the gap between current research and the available large-scale scientific knowledge.
Transformers and the representation of biomedical background knowledge
Specialised transformers-based models (such as BioBERT and BioMegatron) are adapted for the biomedical domain based on publicly available biomedical corpora.
Decomposing Natural Logic Inferences in Neural NLI
In the interest of interpreting neural NLI models and their reasoning strategies, we carry out a systematic probing study which investigates whether these models capture the crucial semantic features central to natural logic: monotonicity and concept inclusion.
Grounding Natural Language Instructions: Can Large Language Models Capture Spatial Information?
Even though BERT and similar pre-trained language models have excelled in several NLP tasks, their use has not been widely explored for the UI grounding domain.
Hybrid Autoregressive Inference for Scalable Multi-hop Explanation Regeneration
Regenerating natural language explanations in the scientific domain has been proposed as a benchmark to evaluate complex multi-hop and explainable inference.
Supporting Context Monotonicity Abstractions in Neural NLI Models
Natural language contexts display logical regularities with respect to substitutions of related concepts: these are captured in a functional order-theoretic property called monotonicity.
Diff-Explainer: Differentiable Convex Optimization for Explainable Multi-hop Inference
This paper presents Diff-Explainer, the first hybrid framework for explainable multi-hop inference that integrates explicit constraints with neural architectures through differentiable convex optimization.
Does My Representation Capture X? Probe-Ably
Probing (or diagnostic classification) has become a popular strategy for investigating whether a given set of intermediate features is present in the representations of neural models.
STAR: Cross-modal [STA]tement [R]epresentation for selecting relevant mathematical premises
Mathematical statements written in natural language are usually composed of two different modalities: mathematical elements and natural language.
Premise Selection in Natural Language Mathematical Texts
The discovery of supporting evidence for addressing complex mathematical problems is a semantically challenging task, which is still unexplored in the field of natural language processing for mathematical text.
Natural Language Premise Selection: Finding Supporting Statements for Mathematical Text
Mathematical text is written using a combination of words and mathematical expressions.
