Natural Language Inference Edit Task
Natural Language Processing
About
Natural language inference 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. |
Benchmarks
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Libraries
Subtasks
Datasets
Latest papers without code
BreakingBERT@IITK at SemEval-2021 Task 9 : Statement Verification and Evidence Finding with Tables
Given a table and a statement/fact, subtask A determines whether the statement is inferred from the tabular data, and subtask B determines which cells in the table provide evidence for the former subtask.
You Can Do Better! If You Elaborate the Reason When Making Prediction
The experimental results show that the proposed approach can generate reasonable explanations for its predictions even with a small-scale training explanation text.
Thinking Aloud: Dynamic Context Generation Improves Zero-Shot Reasoning Performance of GPT-2
Thinking aloud is an effective meta-cognitive strategy human reasoners apply to solve difficult problems.
Unsupervised Contextual Paraphrase Generation using Lexical Control and Reinforcement Learning
Given that the agents as well as the customers can have varying levels of literacy, the overall quality of responses provided by the agents tend to be poor if they are not predefined.
NATURAL LANGUAGE INFERENCE PARAPHRASE GENERATION SEMANTIC SIMILARITY SEMANTIC TEXTUAL SIMILARITY
SML: a new Semantic Embedding Alignment Transformer for efficient cross-lingual Natural Language Inference
NLI is one of the best scenarios to test these architectures, due to the knowledge required to understand complex sentences and established a relation between a hypothesis and a premise.
Robustly Optimized and Distilled Training for Natural Language Understanding
In this paper, we explore multi-task learning (MTL) as a second pretraining step to learn enhanced universal language representation for transformer language models.
KNOWLEDGE DISTILLATION MACHINE READING COMPREHENSION MULTI-TASK LEARNING NATURAL LANGUAGE INFERENCE NATURAL LANGUAGE UNDERSTANDING
Meta-Learning with MAML on Trees
Sharing information between unrelated tasks might hurt performance, and it is unclear how to transfer knowledge across tasks with a hierarchical structure.
CROSS-LINGUAL NATURAL LANGUAGE INFERENCE CROSS-LINGUAL TRANSFER HIERARCHICAL STRUCTURE META-LEARNING NATURAL LANGUAGE UNDERSTANDING
Overcoming Poor Word Embeddings with Word Definitions
Modern natural language understanding models depend on pretrained subword embeddings, but applications may need to reason about words that were never or rarely seen during pretraining.
NATURAL LANGUAGE INFERENCE NATURAL LANGUAGE UNDERSTANDING WORD EMBEDDINGS
Beyond Fully-Connected Layers with Quaternions: Parameterization of Hypercomplex Multiplications with $1/n$ Parameters
Recent works have demonstrated reasonable success of representation learning in hypercomplex space.
4 MACHINE TRANSLATION NATURAL LANGUAGE INFERENCE REPRESENTATION LEARNING STYLE TRANSFER TEXT STYLE TRANSFER
Have Attention Heads in BERT Learned Constituency Grammar?
Our results suggest that SMS tasks decrease the average CGI ability of upper layers, while NLI tasks increase it.