• Corpus ID: 3656972

Natural Language Inference over Interaction Space: ICLR 2018 Reproducibility Report

@article{Mirakyan2018NaturalLI,
  title={Natural Language Inference over Interaction Space: ICLR 2018 Reproducibility Report},
  author={Martin Mirakyan and Karen Hambardzumyan and Hrant Khachatrian},
  journal={ArXiv},
  year={2018},
  volume={abs/1802.03198}
}
We have tried to reproduce the results of the paper "Natural Language Inference over Interaction Space" submitted to ICLR 2018 conference as part of the ICLR 2018 Reproducibility Challenge. Initially, we were not aware that the code was available, so we started to implement the network from scratch. We have evaluated our version of the model on Stanford NLI dataset and reached 86.38% accuracy on the test set, while the paper claims 88.0% accuracy. The main difference, as we understand it, comes… 

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References

SHOWING 1-4 OF 4 REFERENCES

Natural Language Inference over Interaction Space

DIIN, a novel class of neural network architectures that is able to achieve high-level understanding of the sentence pair by hierarchically extracting semantic features from interaction space, shows that an interaction tensor (attention weight) contains semantic information to solve natural language inference.

A large annotated corpus for learning natural language inference

The Stanford Natural Language Inference corpus is introduced, a new, freely available collection of labeled sentence pairs, written by humans doing a novel grounded task based on image captioning, which allows a neural network-based model to perform competitively on natural language inference benchmarks for the first time.

A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference

The Multi-Genre Natural Language Inference corpus is introduced, a dataset designed for use in the development and evaluation of machine learning models for sentence understanding and shows that it represents a substantially more difficult task than does the Stanford NLI corpus.

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The Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion, and has several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters.