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. |
The Stanford Natural Language Inference (SNLI) Corpus contains around 550k hypothesis/premise pairs. Models are evaluated based on accuracy.
State-of-the-art results can be seen on the SNLI website.
The Multi-Genre Natural Language Inference (MultiNLI) corpus contains around 433k hypothesis/premise pairs. It is similar to the SNLI corpus, but covers a range of genres of spoken and written text and supports cross-genre evaluation. The data can be downloaded from the MultiNLI website.
Public leaderboards for in-genre (matched) and cross-genre (mismatched) evaluation are available, but entries do not correspond to published models.
| Model | Matched | Mismatched | Paper / Source | Code |
|---|---|---|---|---|
| Finetuned Transformer LM (Radford et al., 2018) | 82.1 | 81.4 | Improving Language Understanding by Generative Pre-Training | |
| Multi-task BiLSTM + Attn (Wang et al., 2018) | 72.2 | 72.1 | GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding | |
| GenSen (Subramanian et al., 2018) | 71.4 | 71.3 | Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning |
The SciTail entailment dataset consists of 27k. In contrast to the SNLI and MultiNLI, it was not crowd-sourced but created from sentences that already exist "in the wild". Hypotheses were created from science questions and the corresponding answer candidates, while relevant web sentences from a large corpus were used as premises. Models are evaluated based on accuracy.
| Model | Accuracy | Paper / Source |
|---|---|---|
| Finetuned Transformer LM (Radford et al., 2018) | 88.3 | Improving Language Understanding by Generative Pre-Training |
| Hierarchical BiLSTM Max Pooling (Talman et al., 2018) | 86.0 | Natural Language Inference with Hierarchical BiLSTM Max Pooling |
| CAFE (Tay et al., 2018) | 83.3 | A Compare-Propagate Architecture with Alignment Factorization for Natural Language Inference |