Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence 2018
DOI: 10.24963/ijcai.2018/561
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Attention-Fused Deep Matching Network for Natural Language Inference

Abstract: Natural language inference aims to predict whether a premise sentence can infer another hypothesis sentence. Recent progress on this task only relies on a shallow interaction between sentence pairs, which is insufficient for modeling complex relations. In this paper, we present an attention-fused deep matching network (AF-DMN) for natural language inference. Unlike existing models, AF-DMN takes two sentences as input and iteratively learns the attention-aware representations for each side by multi-level intera… Show more

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Cited by 46 publications
(46 citation statements)
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“…10 We leave additional NLI datasets, such as the Diverse NLI Collection (Poliak et al, 2018a), for future work. 11 Many NLI models encode P and H separately (Rocktäschel et al, 2016;Mou et al, 2016;Liu et al, 2016;Cheng et al, 2016;Chen et al, 2017), although some share information between the encoders via attention Duan et al, 2018). 12 Specifically, representations are concatenated, subtracted, and multiplied element-wise.…”
Section: Methods 2: Negative Samplingmentioning
confidence: 99%
“…10 We leave additional NLI datasets, such as the Diverse NLI Collection (Poliak et al, 2018a), for future work. 11 Many NLI models encode P and H separately (Rocktäschel et al, 2016;Mou et al, 2016;Liu et al, 2016;Cheng et al, 2016;Chen et al, 2017), although some share information between the encoders via attention Duan et al, 2018). 12 Specifically, representations are concatenated, subtracted, and multiplied element-wise.…”
Section: Methods 2: Negative Samplingmentioning
confidence: 99%
“…• AF-DMN (Duan et al, 2018) stacks multiple computational blocks in its matching layer to learn the interaction of the sentence pair better.…”
Section: Models For Comparingmentioning
confidence: 99%
“…Tay et al (2018) compare and compress alignment pairs using factorization lay-ers which leverages the rich history of standard machine learning literature to achieve this task. AF-DMN (Duan et al, 2018) stacks multiple computational blocks in its matching layer to learn the interaction of the sentence pair better. KIM is capable of leveraging external knowledge in co-attention, local inference collection, and inference composition components to improve the performance.…”
Section: Related Workmentioning
confidence: 99%
“…The technique has applications in natural language inference to judge whether a hypothesis sentence can be inferred from a premise sentence (Bowman et al, 2015) and in paraphrase identification to determine whether two sentences express the equivalent meaning or not (Yin et al, 2015). The core issue for sentence matching is to model the relatedness between two sentences (Rocktäschel et al, 2015;Parikh et al, 2016;Wang et al, 2017;Duan et al, 2018).…”
Section: Introductionmentioning
confidence: 99%
“…Recently, neural network-based models for sentence matching have attracted more attention for their powerful ability to learn sentence representation (Bowman et al, 2015;Wang et al, 2017;Duan et al, 2018). There are mainly two types of frameworks: sentence encoding based framework and attention-based framework.…”
Section: Introductionmentioning
confidence: 99%