Natural Language Inference
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. |
State-of-the-art leaderboards
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Latest papers without code
Align, Mask and Select: A Simple Method for Incorporating Commonsense Knowledge into Language Representation Models
In this paper, we develop a pre-training approach for incorporating commonsense knowledge into language representation models.
NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SENTENCE CLASSIFICATION
Abductive Commonsense Reasoning
For example, if Jenny finds her house in a mess when she returns from work, and remembers that she left a window open, she can hypothesize that a thief broke into her house and caused the mess, as the most plausible explanation.
Reasoning-Driven Question-Answering for Natural Language Understanding
In the second part, we propose two new challenge datasets.
COMMON SENSE REASONING NATURAL LANGUAGE INFERENCE QUESTION ANSWERING READING COMPREHENSION
StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding
Recently, the pre-trained language model, BERT, has attracted a lot of attention in natural language understanding (NLU), and achieved state-of-the-art accuracy in various NLU tasks, such as sentiment classification, natural language inference, semantic textual similarity and question answering.
LANGUAGE MODELLING NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SEMANTIC TEXTUAL SIMILARITY SENTIMENT ANALYSIS
Do Neural Language Representations Learn Physical Commonsense?
Humans understand language based on the rich background knowledge about how the physical world works, which in turn allows us to reason about the physical world through language.
Fill the GAP: Exploiting BERT for Pronoun Resolution
In this paper, we describe our entry in the gendered pronoun resolution competition which achieved fourth place without data augmentation.
Explaining Simple Natural Language Inference
The vast amount of research introducing new corpora and techniques for semi-automatically annotating corpora shows the important role that datasets play in today{'}s research, especially in the machine learning community.
Probing Multilingual Sentence Representations With X-Probe
This paper extends the task of probing sentence representations for linguistic insight in a multilingual domain.
Can Neural Networks Understand Monotonicity Reasoning?
Monotonicity reasoning is one of the important reasoning skills for any intelligent natural language inference (NLI) model in that it requires the ability to capture the interaction between lexical and syntactic structures.
Testing the Generalization Power of Neural Network Models across NLI Benchmarks
We show that models trained on a natural language inference dataset drawn from one benchmark fail to perform well in others, even if the notion of inference assumed in these benchmarks is the same or similar.