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: Evaluating FastText, GloVe and word2vec, we show that compared to other word representation learning algorithms, the FastText performs best.

SOLUTION: What do you use to calculate word/sub-word embeddings

PROBLEM: For the task-oriented system, although there are some objective evaluation metrics, such as the number of turns in a dialogue, the ratio of task completion, etc., there is no gold standard for automatically evaluating two (or more) dialogue systems when considering the satisfaction of the human and the fluency of the generated dialogue.

SOLUTION: What problems are found with the evaluation scheme?

PROBLEM: Because our conversion rules are interpretable, we identify shortcomings in both resources, using each as validation for the other. We were able to find specific instances of incorrectly applied UniMorph annotation, as well as specific instances of cross-lingual inconsistency in both resources.

SOLUTION:
Do they look for inconsistencies between different languages' annotations in UniMorph?