Convolutional Interaction Network for Natural Language Inference

By Jingjing Gong, Xipeng Qiu, Xinchi Chen, Dong Liang, Xuanjing Huang. 2018

14 citations recorded by the source corpus.

Attention-based neural models have achieved great success in natural language inference (NLI). In this paper, we propose the Convolutional Interaction Network (CIN), a general model to capture the interaction between two sentences, which can be an alternative to the attention mechanism for NLI. Specifically, CIN encodes one sentence with the filters dynamically generated based on another sentence. Since the filters may be designed to have various numbers and sizes, CIN can capture more complicated interaction patterns. Experiments on three large datasets demonstrate CIN’s efficacy.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing, Model Architectures

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Citations in the corpus

Pay Less Attention with Lightweight and Dynamic Convolutions (2019, methodology): “Shen et al. (2018a) and Gong et al. (2018) use input-dependent ﬁlters for text classiﬁcation tasks.”