SlowGuess/ACL_2020_OCR huggingface dataset:
For the remaining questions, we use our QA model to answer each question and we remove questions for which the QA model deems unanswerable. We then take the top 20 most probable questions, random sampling some of the filtered questions if there were too few.
Question Answering We fine-tune BERT for question answering following the original work. Similar to the QG setting, we append the question and answer to the source article with intervening special marker tokens. We optimize using AdamW (Loshchilov and Hutter, 2018) with initial learning rate 5e-5. We train for 3 epochs, with a warmup ratio of 0.1. We use the model with the best development set performance.
Figure 2: Annotation interface and instructions for CNN/DM factual consistency task.
Is the sentence factually supported by the article?
Please provide a justification for your choice (a few words or a sentence).
Please enter here...
Figure 3: Annotation interface and instructions for XSUM factual consistency task.
5020