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Probabilistic Graph Reasoning for Natural Proof Generation ...
Abstract: Read paper: https://www.aclanthology.org/2021.findings-acl.277 Abstract: In this paper, we investigate reasoning over natural language based on modern neural networks. Prior efforts such as PRover train the question answering module and proof generation module through a multi-task classification framework, which does not consider explicit inter-dependency among nodes and edges in the proof graph. In this paper, we propose PRobr, a novel approach for answer prediction and proof generation. PRobr defines a joint probabilistic distribution over all possible proofs and answers via an induced graphical model. We then optimize the model using variational approximation on top of neural textual representation. Experiments on several datasets verify the effectiveness of PRobr under multiple diverse settings (fully supervised, few-shot and zero-shot evaluation), e.g., achieving 10\%-30\% improvement on QA accuracy in few/zero-shot evaluation. ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://dx.doi.org/10.48448/1sqc-2y28
https://underline.io/lecture/26368-probabilistic-graph-reasoning-for-natural-proof-generation
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CoNLL 2018 Shared Task System Outputs
Zeman, Daniel; Potthast, Martin; Duthoo, Elie. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2018
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CoNLL 2017 Shared Task System Outputs
Zeman, Daniel; Potthast, Martin; Straka, Milan. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2017
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