Natural Language Inference
653 papers with code • 44 benchmarks • 76 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:
Benchmarks
These leaderboards are used to track progress in Natural Language Inference
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Libraries
Use these libraries to find Natural Language Inference models and implementations
GLUE
MultiNLI
SNLI
QNLI
MRPC
SuperGLUE
XNLI
SICK
ANLI
WinoGrande
Most implemented papers
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
google-research/bert
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NAACL 2019
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
RoBERTa: A Robustly Optimized BERT Pretraining Approach
pytorch/fairseq
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Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging.
A Structured Self-attentive Sentence Embedding
jadore801120/attention-is-all-you-need-pytorch
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This paper proposes a new model for extracting an interpretable sentence embedding by introducing self-attention.
Deep contextualized word representations
We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e. g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i. e., to model polysemy).
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
google-research/ALBERT
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ICLR 2020
Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks.
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
huggingface/transformers
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arXiv 2019
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP).
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
huggingface/transformers
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ACL 2020
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.
Language Models are Few-Shot Learners
By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do.
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
huggingface/transformers
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NeurIPS 2019
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.
XLNet: Generalized Autoregressive Pretraining for Language Understanding
zihangdai/xlnet
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NeurIPS 2019
With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling.
