TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK
Natural Language Inference	SNLI	1024D GRU encoders w/ unsupervised 'skip-thoughts' pre-training	% Test Accuracy	81.4	# 89
Natural Language Inference	SNLI	1024D GRU encoders w/ unsupervised 'skip-thoughts' pre-training	% Train Accuracy	98.8	# 3
Natural Language Inference	SNLI	1024D GRU encoders w/ unsupervised 'skip-thoughts' pre-training	Parameters	15m	# 4

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Order-Embeddings of Images and Language

19 Nov 2015 · Ivan Vendrov, Ryan Kiros, Sanja Fidler, Raquel Urtasun ·

Hypernymy, textual entailment, and image captioning can be seen as special cases of a single visual-semantic hierarchy over words, sentences, and images. In this paper we advocate for explicitly modeling the partial order structure of this hierarchy. Towards this goal, we introduce a general method for learning ordered representations, and show how it can be applied to a variety of tasks involving images and language. We show that the resulting representations improve performance over current approaches for hypernym prediction and image-caption retrieval.

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ivendrov/order-embedding

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iesl/geometric_graph_embedding

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Cross-Modal Retrieval

Image Captioning

Natural Language Inference

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ImageNet

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SNLI

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Ranked #88 on Natural Language Inference on SNLI

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Task	Dataset	Model	Metric Name	Metric Value	Global Rank	Benchmark
Natural Language Inference	SNLI	1024D GRU encoders w/ unsupervised 'skip-thoughts' pre-training	% Test Accuracy	81.4	# 89	Compare
			% Train Accuracy	98.8	# 3	Compare
			Parameters	15m	# 4	Compare

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