Prediction or Comparison: Toward Interpretable Qualitative Reasoning
| Autor: | Heyan Huang, Yang Gao, Mucheng Ren |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Parsing Computer Science - Computation and Language Generalization Process (engineering) Property (programming) Computer science business.industry computer.software_genre Qualitative reasoning Question answering Artificial intelligence business computer Computation and Language (cs.CL) Natural language processing Natural language Interpretability |
| Zdroj: | ACL/IJCNLP (Findings) |
| Popis: | Qualitative relationships illustrate how changing one property (e.g., moving velocity) affects another (e.g., kinetic energy) and constitutes a considerable portion of textual knowledge. Current approaches use either semantic parsers to transform natural language inputs into logical expressions or a "black-box" model to solve them in one step. The former has a limited application range, while the latter lacks interpretability. In this work, we categorize qualitative reasoning tasks into two types: prediction and comparison. In particular, we adopt neural network modules trained in an end-to-end manner to simulate the two reasoning processes. Experiments on two qualitative reasoning question answering datasets, QuaRTz and QuaRel, show our methods' effectiveness and generalization capability, and the intermediate outputs provided by the modules make the reasoning process interpretable. 12 pages. Accepted as ACL2021 Findings |
| Databáze: | OpenAIRE |
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