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: We test our proposed approach for binary classification on either sarcasm or irony, on seven benchmark datasets retrieved from different media sources. Below we describe each dataset, please see Table TABREF1 below for a summary. Twitter: We use the Twitter dataset provided for the SemEval 2018 Task 3, Irony Detection in English Tweets BIBREF18 Finally, we use the dataset by BIBREF20 , who collected a user self-annotated corpus of tweets with the #sarcasm hashtag. Reddit: BIBREF21 collected SARC, a corpus comprising of 600.000 sarcastic comments on Reddit. We use main subset, SARC 2.0, and the political subset, SARC 2.0 pol. Online Dialogues: We utilize the Sarcasm Corpus V1 (SC-V1) and the Sarcasm Corpus V2 (SC-V2), which are subsets of the Internet Argument Corpus (IAC). Compared to other datasets in our selection, these differ mainly in text length and structure complexity BIBREF22 . To augment each dataset with our external data, we first filter out tweets that are not in English using language guessing systems.
Question: Do they evaluate only on English?
Student:
Yes