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: Models include both neural nets (e.g. RNNs, CNNs) and standard machine learning tools (e.g. Naive Bayes with Laplace Smoothing, k-clustering, SVM with linear kernel).
Question: Which machine learning models are used?

SOLUTION: RNNs CNNs Naive Bayes with Laplace Smoothing k-clustering SVM with linear kernel

PROBLEM: Our system, including software and corpus, is available as an open source project for free research purpose and we believe that it is a good baseline for the development and comparison of future Vietnamese SRL systems.
Question: Are their corpus and software public?

SOLUTION: Yes

PROBLEM: To create training data for unanswerable question generation, we use (plausible) answer spans in paragraphs as pivots to align pairs of answerable questions and unanswerable questions. In this way, we obtain the data with which the models can learn to ask unanswerable questions by editing answerable ones with word exchanges, negations, etc.
Question: How do they ensure the generated questions are unanswerable?

SOLUTION:
learn to ask unanswerable questions by editing answerable ones with word exchanges, negations, etc