nlx-group/Shortcutted-Commonsense-Reasoning
Code for the article "Shortcutted Commonsense: Data Spuriousness in Deep Learning of Commonsense Reasoning", Outstanding Paper at EMNLP2021
This project helps AI researchers and practitioners evaluate if their deep learning models for commonsense reasoning are truly learning robust generalizations or just exploiting superficial patterns in their training data. You provide your Transformer models and commonsense datasets, and the project outputs insights into whether your models are truly understanding the underlying problems or relying on "shortcuts." It's for anyone building or evaluating AI systems that require human-like common sense.
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Use this if you are a researcher or AI engineer who wants to rigorously test the robustness and genuine reasoning capabilities of your natural language processing models, especially those built on Transformers for commonsense tasks.
Not ideal if you are looking for an out-of-the-box solution to directly improve model performance without an interest in deep analytical insights into data spuriousness or shortcut learning.
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Jupyter Notebook
License
MIT
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Last pushed
Nov 07, 2021
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