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 [BibTeX] [Marc21]
Diff-Explainer: Differentiable Convex Optimization for Explainable Multi-Hop Inference
Type of publication: Journal paper
Citation: Thayaparan_TACL_2022
Publication status: Published
Journal: Transactions of the Association for Computational Linguistics
Year: 2022
DOI: https://doi.org/10.1162/tacl_a_00508
Abstract: 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. Specifically, Diff-Explainer allows for the fine-tuning of neural representations within a constrained optimization framework to answer and explain multi-hop questions in natural language. To demonstrate the efficacy of the hybrid framework, we combine existing ILP-based solvers for multi-hop Question Answering (QA) with Transformer-based representations. An extensive empirical evaluation on scientific and commonsense QA tasks demonstrates that the integration of explicit constraints in a end-to-end differentiable framework can significantly improve the performance of non-differentiable ILP solvers (8.91%–13.3%). Moreover, additional analysis reveals that Diff-Explainer is able to achieve strong performance when compared to standalone Transformers and previous multi-hop approaches while still providing structured explanations in support of its predictions.
Projects Idiap
Authors Thayaparan, Mokanarangan
Valentino, Marco
Mendes, Deborah
Rozanova, Julia
Freitas, Andre
Added by: [UNK]
Total mark: 0