Given the task definition, example input & output, solve the new input case.
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: 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
Based on the context, the dataset is constructed from a famous Chinese social media microblogging service Weibo.

New input case for you: Motivated by this, we introduce resolution mode variables $\Pi = \lbrace \pi _1, \ldots , \pi _n
brace $ , where for each mention $j$ the variable $\pi _j \in \lbrace str, prec, attr
brace $ indicates in which mode the mention should be resolved. In our model, we define three resolution modes string-matching (str), precise-construct (prec), and attribute-matching (attr) and $\Pi $ is deterministic when $D$ is given (i.e. $P(\Pi |D)$ is a point distribution). We determine $\pi _j$ for each mention $m_j$ in the following way:

$\pi _j = str$ , if there exists a mention $m_i, i < j$ such that the two mentions satisfy the String Match sieve, the Relaxed String Match sieve, or the Strict Head Match A sieve in the Stanford multi-sieve system BIBREF1 .

$\pi _j = prec$ , if there exists a mention $m_i, i < j$ such that the two mentions satisfy the Speaker Identification sieve, or the Precise Constructs sieve.

$\pi _j = attr$ , if there is no mention $m_i, i < j$ satisfies the above two conditions.
Question: What are resolution model variables?
Output:
Variables in the set {str, prec, attr} indicating in which mode the mention should be resolved.