Natural Language Inference
744 papers with code • 34 benchmarks • 77 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
WinoGrande
XNLI
SICK
ANLI
PAWS
Most implemented papers
Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization
Distributionally robust optimization (DRO) allows us to learn models that instead minimize the worst-case training loss over a set of pre-defined groups.
Reasoning about Entailment with Neural Attention
We extend this model with a word-by-word neural attention mechanism that encourages reasoning over entailments of pairs of words and phrases.
Multi-Task Deep Neural Networks for Natural Language Understanding
In this paper, we present a Multi-Task Deep Neural Network (MT-DNN) for learning representations across multiple natural language understanding (NLU) tasks.
TinyBERT: Distilling BERT for Natural Language Understanding
huawei-noah/Pretrained-Language-Model
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Findings of the Association for Computational Linguistics 2020
To accelerate inference and reduce model size while maintaining accuracy, we first propose a novel Transformer distillation method that is specially designed for knowledge distillation (KD) of the Transformer-based models.
ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations
sinovation/ZEN
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Findings of the Association for Computational Linguistics 2020
Moreover, it is shown that reasonable performance can be obtained when ZEN is trained on a small corpus, which is important for applying pre-training techniques to scenarios with limited data.
FlauBERT: Unsupervised Language Model Pre-training for French
Language models have become a key step to achieve state-of-the art results in many different Natural Language Processing (NLP) tasks.
mT5: A massively multilingual pre-trained text-to-text transformer
google-research/multilingual-t5
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NAACL 2021
The recent "Text-to-Text Transfer Transformer" (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP tasks.
SpanBERT: Improving Pre-training by Representing and Predicting Spans
facebookresearch/SpanBERT
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TACL 2020
We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text.
Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment
Machine learning algorithms are often vulnerable to adversarial examples that have imperceptible alterations from the original counterparts but can fool the state-of-the-art models.
Supervised Multimodal Bitransformers for Classifying Images and Text
Self-supervised bidirectional transformer models such as BERT have led to dramatic improvements in a wide variety of textual classification tasks.
