In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.

Example Input: Pre-trained English NER model We construct the English NER system following BIBREF7 . This system uses a bidirectional LSTM as a character-level language model to take context information for word embedding generation. For pre-trained English NER system, we use the default NER model of Flair.
Question: Which pre-trained English NER model do they use?
Example Output: Bidirectional LSTM based NER model of Flair

Example Input: We conduct experiments on our self-collected CAIS to evaluate the generalizability in different language. We apply two baseline models for comparison, one is the popular BiLSTMs + CRF architecture BIBREF36 for sequence labeling task, and the other one is the more powerful sententce-state LSTM BIBREF21. The results listed in Table TABREF50 demonstrate the generalizability and effectiveness of our CM-Net when handling various domains and different languages.
Question: What were the baselines models?
Example Output: BiLSTMs + CRF architecture BIBREF36 sententce-state LSTM BIBREF21

Example Input: Experimental Setup
Question: which chinese datasets were used?
Example Output:
Answer with content missing: (Data section) Chinese with version 5.1 of the Chinese Penn Treebank (CTB)