Teacher: 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.
Teacher: Now, understand the problem? If you are still confused, see the following 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?
Solution: No
Reason: Based on the context, the dataset is constructed from a famous Chinese social media microblogging service Weibo.

Now, solve this instance: The first dataset is an expanded version of the annotated Wikipedia conversations dataset from BIBREF9. This dataset uses carefully-controlled crowdsourced labels, strictly filtered to ensure the conversations are civil up to the moment of a personal attack. This is a useful property for the purposes of model analysis, and hence we focus on this as our primary dataset. However, we are conscious of the possibility that these strict labels may not fully capture the kind of behavior that moderators care about in practice. We therefore introduce a secondary dataset, constructed from the subreddit ChangeMyView (CMV) that does not use post-hoc annotations. Instead, the prediction task is to forecast whether the conversation will be subject to moderator action in the future. Wikipedia data. BIBREF9's 'Conversations Gone Awry' dataset consists of 1,270 conversations that took place between Wikipedia editors on publicly accessible talk pages. The conversations are sourced from the WikiConv dataset BIBREF59 and labeled by crowdworkers as either containing a personal attack from within (i.e., hostile behavior by one user in the conversation directed towards another) or remaining civil throughout. Reddit CMV data. The CMV dataset is constructed from conversations collected via the Reddit API. In contrast to the Wikipedia-based dataset, we explicitly avoid the use of post-hoc annotation. Instead, we use as our label whether a conversation eventually had a comment removed by a moderator for violation of Rule 2: "Don't be rude or hostile to other users".
Question: What labels for antisocial events are available in datasets?
Student:
The Conversations Gone Awry dataset is labelled as either containing a personal attack from withint (i.e. hostile behavior by one user in the conversation directed towards another) or remaining civil throughout. The Reddit Change My View dataset is labelled with whether or not a coversation eventually had a comment removed by a moderator for violation of Rule 2: "Don't be rude or hostile to others users."