reasoning-8
Natural Language Inference
3260 papers • 126 benchmarks • 313 datasets
Natural language inference (NLI) is the task of determining whether a "hypothesis" is true (entailment), false (contradiction), or undetermined (neutral) given a "premise". Example: Premise Label Hypothesis A man inspects the uniform of a figure in some East Asian country. contradiction The man is sleeping. An older and younger man smiling. neutral Two men are smiling and laughing at the cats playing on the floor. A soccer game with multiple males playing. entailment Some men are playing a sport. Approaches used for NLI include earlier symbolic and statistical approaches to more recent deep learning approaches. Benchmark datasets used for NLI include SNLI, MultiNLI, SciTail, among others. You can get hands-on practice on the SNLI task by following this d2l.ai chapter. Further readings: Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Kenton Lee, Kristina Toutanova, Ming-Wei Chang•Mon Dec 31 2018
A new language representation model, BERT, designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers, which can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.
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