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: 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

Example Input: For DQA four participants answered each question, therefore we took the average INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 values over the four evaluators as the result per question. 
 Question: How do they measure performance?
Example Output: average INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 values

Example Input: $\mathit {\texttt {+}PPMI}$ only falters on the RW and analogy tasks, and we hypothesize this is where $\mathit {\texttt {-}PMI}$ is useful: in the absence of positive information, negative information can be used to improve rare word representations and word analogies. 
 Question: What are the disadvantages to clipping negative PMI?
Example Output:
It may lead to poor rare word representations and word analogies.