Attentive Tree-structured Network for Monotonicity Reasoning

• URL: http://arxiv.org/abs/2101.00540v1
• Date: Sun, 3 Jan 2021 01:29:48 GMT
• Title: Attentive Tree-structured Network for Monotonicity Reasoning
• Authors: Zeming Chen
• Abstract summary: We develop an attentive tree-structured neural network for monotonicity reasoning. It is designed to model the syntactic parse tree information from the sentence pair of a reasoning task. A self-attentive aggregator is used for aligning the representations of the premise and the hypothesis.
• Score: 2.4366811507669124
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many state-of-art neural models designed for monotonicity reasoning perform poorly on downward inference. To address this shortcoming, we developed an attentive tree-structured neural network. It consists of a tree-based long-short-term-memory network (Tree-LSTM) with soft attention. It is designed to model the syntactic parse tree information from the sentence pair of a reasoning task. A self-attentive aggregator is used for aligning the representations of the premise and the hypothesis. We present our model and evaluate it using the Monotonicity Entailment Dataset (MED). We show and attempt to explain that our model outperforms existing models on MED.

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