Natural Language Inference
522 papers with code • 32 benchmarks • 64 datasets
Natural language inference (NLI) is the task of determining whether a "hypothesis" is true (entailment), false (contradiction), or undetermined (neutral) given a "premise".
|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.
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging.
Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks.
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of the original sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token.
As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP), operating these large models in on-the-edge and/or under constrained computational training or inference budgets remains challenging.
With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling.
Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features.