A linguistically-motivated evaluation methodology for unraveling model's abilities in reading comprehension tasks
This provides a fine-grained evaluation tool for researchers in natural language processing to better understand model limitations in handling linguistic complexity, though it is incremental as it builds on existing benchmarks.
The authors tackled the problem of evaluating reading comprehension models by introducing a linguistically-motivated methodology that identifies specific complexity factors causing model failures, revealing that two factors consistently predict lower scores regardless of model size or architecture.
We introduce an evaluation methodology for reading comprehension tasks based on the intuition that certain examples, by the virtue of their linguistic complexity, consistently yield lower scores regardless of model size or architecture. We capitalize on semantic frame annotation for characterizing this complexity, and study seven complexity factors that may account for model's difficulty. We first deploy this methodology on a carefully annotated French reading comprehension benchmark showing that two of those complexity factors are indeed good predictors of models' failure, while others are less so. We further deploy our methodology on a well studied English benchmark by using Chat-GPT as a proxy for semantic annotation. Our study reveals that fine-grained linguisticallymotivated automatic evaluation of a reading comprehension task is not only possible, but helps understand models' abilities to handle specific linguistic characteristics of input examples. It also shows that current state-of-the-art models fail with some for those characteristics which suggests that adequately handling them requires more than merely increasing model size.