Natural Language Inference
730 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
Big Bird: Transformers for Longer Sequences
google-research/bigbird
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NeurIPS 2020
To remedy this, we propose, BigBird, a sparse attention mechanism that reduces this quadratic dependency to linear.
A Decomposable Attention Model for Natural Language Inference
We propose a simple neural architecture for natural language inference.
Bilateral Multi-Perspective Matching for Natural Language Sentences
Natural language sentence matching is a fundamental technology for a variety of tasks.
XNLI: Evaluating Cross-lingual Sentence Representations
facebookresearch/XLM
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EMNLP 2018
State-of-the-art natural language processing systems rely on supervision in the form of annotated data to learn competent models.
NEZHA: Neural Contextualized Representation for Chinese Language Understanding
The pre-trained language models have achieved great successes in various natural language understanding (NLU) tasks due to its capacity to capture the deep contextualized information in text by pre-training on large-scale corpora.
DeBERTa: Decoding-enhanced BERT with Disentangled Attention
microsoft/DeBERTa
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ICLR 2021
Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks.
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
pytorch/fairseq
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Preprint 2022
While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind.
ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs
(ii) We propose three attention schemes that integrate mutual influence between sentences into CNN; thus, the representation of each sentence takes into consideration its counterpart.
From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification
deep-spin/entmax
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We propose sparsemax, a new activation function similar to the traditional softmax, but able to output sparse probabilities.
Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence
HSLCY/ABSA-BERT-pair
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NAACL 2019
Aspect-based sentiment analysis (ABSA), which aims to identify fine-grained opinion polarity towards a specific aspect, is a challenging subtask of sentiment analysis (SA).
