TASK DEFINITION: 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.
PROBLEM: 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?

SOLUTION: BiLSTMs + CRF architecture BIBREF36 sententce-state LSTM BIBREF21

PROBLEM: We perform a direct comparison of expert and crowd contributors, for 1000 term groups based on the number of total annotations(200 term groups with 2 total annotations, 200 term groups with 3 total annotations, and so on up to term groups with 6 total annotations).  Evaluators were given a summary of the annotations received for the term group in the form of:The term group "inequality inequity" received annotations as 50.0% sadness, 33.33% disgust, 16.67% anger. Then, they were asked to evaluate on a scale from 1 to 5, how valid these annotations were considered. 
 Question: How do they compare lexicons?

SOLUTION: Human evaluators were asked to evaluate on a scale from 1 to 5 the validity of the lexicon annotations made by the experts and crowd contributors.

PROBLEM: We describe our rules for WikiSQL here. Our rule for KBQA is simple without using a curated mapping dictionary. The pipeline of rules in SequentialQA is similar to that of WikiSQL. 
 Question: Are the rules dataset specific?

SOLUTION:
Yes