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One Sentence Abstract

This study introduces a new iterative, human-and-model-in-the-loop collected NLI benchmark dataset, revealing state-of-the-art performance improvements while exposing current model weaknesses, and showcasing a continuously evolving data collection method for NLU.

Simplified Abstract

Researchers have created a new, large-scale dataset to help improve the understanding of how computers process human language. They collected this data using a special method that involved people and computers working together in an iterative process. This new dataset helped improve the performance of existing language processing models, while also revealing their limitations.

The method used in this study is like a game where humans and computers work together in rounds, with humans providing feedback on the computers' language understanding abilities. This collaborative process helps the computers become better at understanding human language.

The main finding of this research is that the new dataset, collected through this method, can help make language processing models more accurate and reliable. This is important because it helps us understand how countries work together in scientific collaborations, as language is a key factor in understanding and communicating research findings.

This study is significant because it introduces a new approach to creating language processing datasets. Instead of a fixed benchmark, this method creates a "moving target" scenario, constantly challenging the models and preventing them from becoming outdated. This approach improves the accuracy and reliability of the results, making it a valuable contribution to the field of natural language understanding.

Study Fields

Main fields:

• Natural Language Understanding (NLU)
• Natural Language Processing (NLP)

Subfields:

• Dataset collection
• Adversarial human-and-model-in-the-loop procedure
• State-of-the-art performance
• Shortcomings of current models
• Non-expert annotators
• Never-ending learning scenario

Study Objectives

• Develop a new large-scale Natural Language Inference (NLI) benchmark dataset
• Collect data via an iterative, adversarial human-and-model-in-the-loop procedure
• Demonstrate improved performance of trained models on popular NLI benchmarks using the new dataset
• Highlight challenges posed by the new dataset
• Analyze shortcomings of current state-of-the-art models
• Show that non-expert annotators can identify weaknesses in models
• Propose a never-ending learning scenario for the data collection method, making it a dynamic target for Natural Language Understanding (NLU) rather than a static benchmark

Conclusions

• A new large-scale Natural Language Inference (NLI) benchmark dataset is introduced, collected using an iterative, adversarial human-and-model-in-the-loop procedure.
• Training models on this new dataset results in state-of-the-art performance on various popular NLI benchmarks while posing a more difficult challenge.
• The new dataset highlights the limitations of current state-of-the-art models and demonstrates that non-expert annotators can effectively identify their weaknesses.
• The data collection method provides a dynamic approach that can be applied in a never-ending learning scenario, continually challenging Natural Language Understanding (NLU) systems instead of becoming obsolete quickly.

References