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.

Example Input: After removing 135 questions during the validation, the final dataset comprises 13,939 questions, 3,827 of which require commonsense knowledge (i.e. 27.4%).  
 Question: what is the size of their dataset?
Example Output: 13,939

Example Input: It is a well known fact that NLP applications in industrial settings often have to deal with the noisy data. There are different kinds of possible noise namely non-canonical text such as spelling mistakes, typographic errors, colloquialisms, abbreviations, slang, internet jargon, emojis, embedded metadata (such as hashtags, URLs, mentions), non standard syntactic constructions and spelling variations, grammatically incorrect text, mixture of two or more languages to name a few. Such noisy data is a hallmark of user generated text content and commonly found on social media, chats, online reviews, web forums to name a few. 
 Question: What kind is noise is present in typical industrial data?
Example Output:  non-canonical text such as spelling mistakes, typographic errors, colloquialisms, abbreviations, slang, internet jargon, emojis, embedded metadata (such as hashtags, URLs, mentions), non standard syntactic constructions and spelling variations, grammatically incorrect text, mixture of two or more languages

Example Input: We evaluate our model on both English and Chinese segmentation. For both languages we used standard datasets for word segmentation and language modeling. English
The Brent corpus is a standard corpus used in statistical modeling of child language acquisition BIBREF15 , BIBREF16  We use the commonly used version of the PTB prepared by BIBREF17 Since Chinese orthography does not mark spaces between words, there have been a number of efforts to annotate word boundaries. We evaluate against two corpora that have been manually segmented according different segmentation standards. The Beijing University Corpus was one of the corpora used for the International Chinese Word Segmentation Bakeoff BIBREF18 . We use the Penn Chinese Treebank Version 5.1 BIBREF19 . 
 Question: What dataset is used?
Example Output:
Brent corpus PTB  Beijing University Corpus Penn Chinese Treebank