Overcoming the Hurdles: Challenges in Natural Language Processing NLP by Niraj Kumar Nirala
Natural Language Processing: Top Key Challenges & Applications Such a collection needs to be versioned, to enable updates beyond the cycle of academic review and to enable replicability and comparison to prior approaches. In order to better understand the strengths and weaknesses of our models, we furthermore require more fine-grained evaluation across a single metric, highlighting on what types of examples models excel and fail at. ExplainaBoard (Liu et al., 2021) implements such a fine-grained breakdown of model performance across different tasks, which can be seen below. Another way...
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