TASK DEFINITION: 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.
PROBLEM: 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.

SOLUTION: What data is the language model pretrained on?

PROBLEM: The experiment dataset comes from Microsoft Research (MSR) . It contains three domains: movie, taxi, and restaurant. The total count of dialogues per domain and train/valid/test split is reported in Table TABREF11. At every turn both user and agent acts are annotated, we use only the agent side as targets in our experiment. The acts are ordered in the dataset (each output sentence aligns with one act).

SOLUTION: What datasets are used for training/testing models? 

PROBLEM: BIBREF17: The Lemming model is a log-linear model that performs joint morphological tagging and lemmatization. 

SOLUTION:
What were the non-neural baselines used for the task?