Welcome to the second part of our series on natural language inference, where we explore three important datasets: SNLI, MultiNLI, and Adversarial NLI. These datasets serve as benchmark tasks for training diverse systems and have played a significant role in advancing the field of natural language understanding.
Contents
SNLI: The Stanford Natural Language Inference Corpus
SNLI is the first dataset we’ll delve into. It consists of image captions from the Flickr30K dataset, serving as premises, and hypotheses written by crowdworkers. The goal is to predict the relationship between these two texts using three labels: entailment, neutral, and contradiction. However, it’s worth noting that due to the nature of crowdsourced data, SNLI does contain sentences that reflect stereotypes. Nonetheless, it’s a valuable resource with over 550,000 training examples and dev and test sets each with 10,000 balanced examples across the three classes.
MultiNLI: A Diverse Dataset for Natural Language Inference
MultiNLI is a successor to SNLI and aims to address the overfitting issue seen in SNLI by diversifying the genres of the premises. It includes data from various sources, such as fiction, government reports, letters, websites, and more. Additionally, MultiNLI introduces a “mismatched condition” where models trained on one genre must perform on entirely new genres during testing. This challenging setup evaluates the robustness and generalization capabilities of systems. With slightly fewer examples than SNLI, MultiNLI encourages the development of models that can handle longer sentences and varying linguistic phenomena.
Adversarial NLI: Testing the Boundaries of NLI Models
To further scrutinize the progress made in NLI, the Adversarial NLI project was initiated. Through this project, crowdworkers generate hypotheses specifically designed to fool state-of-the-art models. The dataset is expanded over multiple rounds, with each round increasing the difficulty of examples by training models on previous round data. Adversarial NLI has led to the development of the Dynabench project, which aims to create more adversarial datasets across different domains, encouraging the advancement of NLI benchmarks.
Exploring the NLI Landscape
Beyond SNLI, MultiNLI, and Adversarial NLI, there are numerous other NLI datasets worth exploring. The GLUE and SuperGLUE benchmarks offer a range of NLI tasks, while FEVER focuses on fact verification through NLI-style examples. Additionally, there are NLI corpora available for Chinese and Turkish languages, providing opportunities for multilingual NLI research. For specific domains like medicine and science, there are dedicated NLI datasets, enabling evaluation in specialized areas.
With this wide array of tasks, the NLI domain presents an exciting space for developing original systems and projects. It is a testament to the rapid progress in NLI and the continuous efforts of the community to bring us closer to assessing the true capabilities of natural language understanding systems.
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