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.

Our data set contains $3,209$ reviews about 553 different cars from 49 different car manufacturers. 
 Question: How big is dataset of car-speak language?
$3,209$ reviews 

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

Despite its usefulness, linked entities extracted from ELS's have issues because of low precision rates BIBREF11 and design challenges in training datasets BIBREF12 . First, the extracted entities may be ambiguous. Second, the linked entities may also be too common to be considered an entity.  
 Question: Why are current ELS's not sufficiently effective?

Linked entities may be ambiguous or too common