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.

Ex Input:
We have evaluated the output of Nefnir against a reference corpus of 21,093 tokens and their correct lemmas.

Samples for the reference corpus were extracted from two larger corpora, in order to obtain a diverse vocabulary: 
 Question: Which dataset do they use?

Ex Output:
a reference corpus of 21,093 tokens and their correct lemmas


Ex Input:
 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?

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


Ex Input:
We first use predictors based on rules that have previously been proposed in the literature: word length, number of phonemes, number of syllables, alphabetical order, and frequency.  
 Question: What previously proposed rules for predicting binoial ordering are used?

Ex Output:

word length, number of phonemes, number of syllables, alphabetical order, and frequency