[PDF][PDF] Enhancing and combining sequential and tree lstm for natural language inference
Abstract Reasoning and inference are central to human and artificial intelligence. Modeling
inference in human language is notoriously challenging but is fundamental to natural
language understanding and many applications. With the availability of large annotated
data, neural network models have recently advanced the field significantly. In this paper, we
present a new state-of-the-art result, achieving the accuracy of 88.3% on the standard
benchmark, the Stanford Natural Language Inference dataset. This result is achieved first …
inference in human language is notoriously challenging but is fundamental to natural
language understanding and many applications. With the availability of large annotated
data, neural network models have recently advanced the field significantly. In this paper, we
present a new state-of-the-art result, achieving the accuracy of 88.3% on the standard
benchmark, the Stanford Natural Language Inference dataset. This result is achieved first …
[CITATION][C] Enhancing and combining sequential and tree LSTM for natural language inference. CoRR abs/1609.06038 (2016)
Q Chen, X Zhu, Z Ling, S Wei, H Jiang - arXiv preprint arXiv:1609.06038, 2016
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