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Hi all,
I'm using Flair's Semantic Frame tagger for analyzing learner language in my research. While the model performs well qualitatively, I'm struggling to establish robust evaluation metrics for this specific use case. The main challenges are that we lack gold-standard annotated learner corpora with frame semantic annotations, there are potential discrepancies between standard frame patterns and those found in learner language, and we need to account for variations in frame realization due to learner errors.
One particular challenge is communicating the model's reliability to reviewers who aren't familiar with automated NLP tagging approaches like Flair. Without established evaluation metrics for this specific context, it's difficult to demonstrate the validity of the results in a way that's convincing to researchers from different backgrounds.
I'm looking for suggestions on recommended evaluation approaches beyond standard F1 scores, experience with creating evaluation datasets for non-standard language varieties, and methods to assess frame assignment reliability when dealing with unconventional syntactic structures. Any insights on how others have validated their frame semantic analysis and effectively communicated their results to non-NLP audiences would be especially valuable.
Has anyone tackled similar challenges or can point me to relevant evaluation frameworks?
The text was updated successfully, but these errors were encountered:
Question
Hi all,
I'm using Flair's Semantic Frame tagger for analyzing learner language in my research. While the model performs well qualitatively, I'm struggling to establish robust evaluation metrics for this specific use case. The main challenges are that we lack gold-standard annotated learner corpora with frame semantic annotations, there are potential discrepancies between standard frame patterns and those found in learner language, and we need to account for variations in frame realization due to learner errors.
One particular challenge is communicating the model's reliability to reviewers who aren't familiar with automated NLP tagging approaches like Flair. Without established evaluation metrics for this specific context, it's difficult to demonstrate the validity of the results in a way that's convincing to researchers from different backgrounds.
I'm looking for suggestions on recommended evaluation approaches beyond standard F1 scores, experience with creating evaluation datasets for non-standard language varieties, and methods to assess frame assignment reliability when dealing with unconventional syntactic structures. Any insights on how others have validated their frame semantic analysis and effectively communicated their results to non-NLP audiences would be especially valuable.
Has anyone tackled similar challenges or can point me to relevant evaluation frameworks?
The text was updated successfully, but these errors were encountered: