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 Q]: Trinomials are likely to appear in exactly one order, and when they appear in more than one order the last word is almost always the same across all instances.  
 Question: Are there any new finding in analasys of trinomials that was not present binomials?
[EX A]: Trinomials are likely to appear in exactly one order

[EX Q]: KGR10, also known as plWordNet Corpus 10.0 (PLWNC 10.0), is the result of the work on the toolchain to automatic acquisition and extraction of the website content, called CorpoGrabber BIBREF19 . It is a pipeline of tools to get the most relevant content of the website, including all subsites (up to the user-defined depth). The proposed toolchain can be used to build a big Web corpus of text documents. It requires the list of the root websites as the input. Tools composing CorpoGrabber are adapted to Polish, but most subtasks are language independent. The whole process can be run in parallel on a single machine and includes the following tasks: download of the HTML subpages of each input page URL with HTTrack, extraction of plain text from each subpage by removing boilerplate content (such as navigation links, headers, footers, advertisements from HTML pages) BIBREF20 , deduplication of plain text BIBREF20 , bad quality documents removal utilising Morphological Analysis Converter and Aggregator (MACA) BIBREF21 , documents tagging using Wrocław CRF Tagger (WCRFT) BIBREF22 . Last two steps are available only for Polish. 
 Question: How was the KGR10 corpus created?
[EX A]: most relevant content of the website, including all subsites

[EX Q]: We represent each instance of image, question, and user choice as a triplet consisting of image feature, question feature, and the label vector for the user's answer. In addition, collecting multiple choices from identical users enables us to represent any two instances by the same user as a pair of triplets, assuming source-target relation. With these pairs of triplets, we can train the system to predict a user's choice on a new image and a new question, given the same user's choice on the previous image and its associated question. As discussed earlier, we attempt to reflect user's interest by asking questions that provide visual context. In other words, a question whose answer is so obvious that it can be answered in an identical way would not be valid as an interactive question.   If the most likely candidate $c_i=\max p_{ans}$ , where $c_i \in C$ , has a probability of being answer over a certain threshold $\alpha $ , then the question is considered to have a single obvious answer, and is thus considered ineligible.  In our experiments, we set $\alpha $ as 0.33. We also excluded the yes/no type of questions.  
 Question: What are the features of used to customize target user interaction? 
[EX A]:
image feature question feature label vector for the user's answer