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.

Q: 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?

A: $3,209$ reviews 
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Q: We evaluate our method on three tagging tasks: POS tagging (Pos), morphological tagging (Morph) and supertagging (Stag). We select these tasks as examples for tagging applications because they differ strongly in tag set sizes. The test results for the three tasks are shown in Table TABREF17 in three groups. The first group of seven columns are the results for Pos, where both LSTM and CNN have three variations of input features: word only ( INLINEFORM0 ), character only ( INLINEFORM1 ) and both ( INLINEFORM2 ). For Morph and Stag, we only use the INLINEFORM3 setting for both LSTM and CNN. 
 Question: Do they jointly tackle multiple tagging problems?

A: No
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Q: A frequency filter ratio INLINEFORM0 is set to filter out the low-frequency words (rare words) from the lookup table 
 Question: how are rare words defined?

A:
low-frequency words
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