In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

The annotation projection method, which we follow in this work, is one way to address this problem. It was introduced for POS tagging, base noun phrase bracketing, NER tagging, and inflectional morphological analysis BIBREF29 but it has also been used for dependency parsing BIBREF30 , role labeling BIBREF31 , BIBREF32 and semantic parsing BIBREF26 .
Do the authors test their annotation projection techniques on tasks other than AMR?

Due to the high cost of pre-training BERT language model, we directly adopt parameters pre-trained by Google in Chinese general corpus. The named entity recognition is applied on both pathology report texts and query texts.
What data is the language model pretrained on?

We use the MedWeb (“Medical Natural Language Processing for Web Document”) dataset BIBREF4 that was provided as part of a subtask at the NTCIR-13 Conference BIBREF5. The data is summarised in Table TABREF1. There are a total of 2,560 pseudo-tweets in three different languages: Japanese (ja), English (en) and Chinese (zh).

How big is dataset used for fine-tuning model for detection of red flag medical symptoms in individual statements?

