MultiNli (Multi-Genre Natural Language Inference Corpus)
MultiNLI (Multi-Genre Natural Language Inference) is a reference data set for evaluating the logical understanding of language by NLP models. It was designed to test the ability of models to determine the relationship between two sentences: involvement, contradiction, or neutrality.
Approximately 400,000 sentence pairs, TSV format
Free for academic use. Restrictions may apply depending on commercial use
Description
The MultiNli dataset includes:
- Approximately 400,000 pairs of manually annotated sentences
- Three logical relationships: bias, contradiction, neutral
- A diversity of textual sources covering formal and informal contexts
- A TSV format that is easy to integrate into traditional NLP pipelines
What is this dataset for?
MultiNli is mainly used for:
- Training textual entailment recognition models
- Assessing the ability of models to detect logical relationships between sentences
- The fine-tuning of language models on contextual comprehension tasks
- Analysis of the robustness and logical coherence of the responses generated by the models
Can it be enriched or improved?
Yes, MultiNli can be enriched or adapted for:
- Create multilingual versions to evaluate models in other languages
- Add metadata about genres or domains for finer filtering
- Combine with SNLI (Stanford NLI) for wider coverage
- Automatically generate new pairs with paraphrase or contradiction models
🔗 Source: MultiNli Dataset
Frequently Asked Questions
What is the difference between MultiNLI and SNLI?
SNLI is focused on a single domain (image descriptions), while MultiNLI covers multiple text genres, making it possible to better test the generalization of models across different language styles.
Can MultiNli be used for evaluation and training?
Yes, it is frequently used both for fine-tuning and for evaluating the logical inference quality of a model.
Why is MultiNli important for generation models?
Even though it's not a generation dataset, MultiNli helps train models to maintain logical consistency in their responses, which is critical for applications like chatbots or voice assistants.
