Definition: 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.

Positive Example 1 -
Input: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese Short Text Summarization Dataset (LCSTS) is constructed by BIBREF1 . The dataset consists of more than 2.4 million text-summary pairs in total, constructed from a famous Chinese social media microblogging service Weibo.  
 Question: Are results reported only for English data?
Output: No

Positive Example 2 -
Input: Since INLINEFORM0 is constant, we only need to minimize INLINEFORM1 , therefore the loss function becomes: DISPLAYFORM0 
 Question: How is the expectation regularization loss defined?
Output: DISPLAYFORM0

Negative Example 1 -
Input: The minimal change operation substantially changed the meaning of the sentence, and yet the embedding of the transformation lies very closely to the original sentence (average similarity of 0.930). 
 Question: Do they do any analysis of of how the modifications changed the starting set of sentences?
Output: No

Negative Example 2 -
Input: Looking at the current WinoGrande leaderboard, it appears that the previous state of the art is based on RoBERTa BIBREF2, which can be characterized as an encoder-only transformer architecture. 
 Question: What is the previous state of the art?
Output: GPT-3

Now complete the following example -
Input: One of the most important operations on a word is to obtain the set of words whose meaning is similar to the word, or whose usage in text is similar to the word. We call this set the neighbor of the word. We have observed that the neighbor of a polysemic word consists of words that resemble the primary sense of the polysemic word.  Even though a word may be a polysemic, it usually corresponds to a single vector in distributed representation. This vector is primarily determined by the major sense, which is most frequently used. The information about a word's minor sense is subtle, and the effect of a minor sense is difficult to distinguish from statistical fluctuation. To measure the effect of a minor sense, this paper proposes to use the concept of surrounding uniformity. The surrounding uniformity roughly corresponds to statistical fluctuation in the vectors that correspond to the words in the neighbor Surrounding Uniformity (SU) can be expressed as follows: $SU(\vec{w}) = \frac{|\vec{s}(\vec{w})|}{|\vec{w}| + \sum _{i}^{N}|\vec{a_i}(\vec{w})|}$

where $\vec{s}(\vec{w}) = \vec{w} + \sum _{i}^{N} \vec{a_i}(\vec{w}).$ 
 Question: How is the fluctuation in the sense of the word and its neighbors measured?
Output: 



Answer: Our method performs a statistical test to determine whether a given word is used polysemously in the text, according to the following steps:
1) Setting N, the size of the neighbor.
2) Choosing N neighboring words ai in the order whose angle with the vector of the given word w is the smallest.
3) Computing the surrounding uniformity for ai(0 < i ≤ N) and w.
4) Computing the mean m and the sample variance σ for the uniformities of ai .
5) Checking whether the uniformity of w is less than m − 3σ. If the value is less than m − 3σ, we may regard w as a polysemic word.