Natural Language Inference
729 papers with code • 43 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
XNLI
WinoGrande
SICK
ANLI
PAWS
Most implemented papers
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
facebookresearch/InferSent
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EMNLP 2017
Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features.
SimCSE: Simple Contrastive Learning of Sentence Embeddings
princeton-nlp/SimCSE
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EMNLP 2021
This paper presents SimCSE, a simple contrastive learning framework that greatly advances state-of-the-art sentence embeddings.
Pay Attention to MLPs
labmlai/annotated_deep_learning_paper_implementations
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NeurIPS 2021
Transformers have become one of the most important architectural innovations in deep learning and have enabled many breakthroughs over the past few years.
ERNIE: Enhanced Representation through Knowledge Integration
We present a novel language representation model enhanced by knowledge called ERNIE (Enhanced Representation through kNowledge IntEgration).
FNet: Mixing Tokens with Fourier Transforms
google-research/google-research
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NAACL 2022
At longer input lengths, our FNet model is significantly faster: when compared to the "efficient" Transformers on the Long Range Arena benchmark, FNet matches the accuracy of the most accurate models, while outpacing the fastest models across all sequence lengths on GPUs (and across relatively shorter lengths on TPUs).
Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond
facebookresearch/LASER
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TACL 2019
We introduce an architecture to learn joint multilingual sentence representations for 93 languages, belonging to more than 30 different families and written in 28 different scripts.
Enhanced LSTM for Natural Language Inference
Reasoning and inference are central to human and artificial intelligence.
SentEval: An Evaluation Toolkit for Universal Sentence Representations
facebookresearch/SentEval
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LREC 2018
We introduce SentEval, a toolkit for evaluating the quality of universal sentence representations.
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
ofa-sys/ofa
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WS 2018
For natural language understanding (NLU) technology to be maximally useful, both practically and as a scientific object of study, it must be general: it must be able to process language in a way that is not exclusively tailored to any one specific task or dataset.
Improving Language Understanding by Generative Pre-Training
huggingface/transformers
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Preprint 2018
We demonstrate that large gains on these tasks can be realized by generative pre-training of a language model on a diverse corpus of unlabeled text, followed by discriminative fine-tuning on each specific task.
