Building Language Models for Text with Named Entities
Table 3: Performance of fill in the blank task.
['Ingredient', 'Train Freq.', '#Blanks', 'Accuracy Free-Form', 'Accuracy Free-Form', 'Accuracy MCQ', 'Accuracy MCQ']
Our model outperforms the fill in the blank task for both cases, i.e., without any options (free-form) and MCQ. Note that, the percentage of improvement is inversely proportional to the training frequencies of the ingredients— less-frequent ingredients achieve a higher accuracy improvement (e.g., “Apple” and “Tomato”). This validates our intuition of learning to predict the type first more accurately with lower vocabulary set and then use conditional probability to predict the actual entity considering the type as a prior.