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: Using a set of semantically oriented tasks that require explicit semantic cross-lingual representations, we showed that mBERT contextual embeddings do not represent similar semantic phenomena similarly and therefore they are not directly usable for zero-shot cross-lingual tasks. 
 Question: What challenges this work presents that must be solved to build better language-neutral representations?

SOLUTION: contextual embeddings do not represent similar semantic phenomena similarly and therefore they are not directly usable for zero-shot cross-lingual tasks

PROBLEM:  We defined a sequence labeling task to extract custom entities from user input. We assumed seven (7) possible entities (see Table TABREF43) to be recognized by the model: topic, subtopic, examination mode and level, question number, intent, as well as the entity other for remaining words in the utterance.   We defined a classification problem to predict the system's next action according to the given user input. We assumed 13 custom actions (see Table TABREF42) that we considered being our labels. In the conversational dataset, each input was automatically labeled by the rule-based system with the corresponding next action and the dialogue-id. Thus, no additional post-labeling was required.  
 Question: How does the IPA label data after interacting with users?

SOLUTION: It defined a sequence labeling task to extract custom entities from user input and label the next action (out of 13  custom actions defined).

PROBLEM: The encoder is a bidirectional Long-Short Term Memory(LSTM) cell BIBREF14 and the decoder a single LSTM cell with attention mechanism.  
 Question: How many attention layers are there in their model?

SOLUTION:
one