You will be given a definition of a task first, then an example. Follow the example to solve a new instance of the task.
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.

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
Why? Based on the context, the dataset is constructed from a famous Chinese social media microblogging service Weibo.

New input: Irony Classifier: We implement a CNN classifier trained with our irony dataset. Sentiment Classifier for Irony: We first implement a one-layer LSTM network to classify ironic sentences in our dataset into positive and negative ironies. Sentiment Classifier for Non-irony: Similar to the training process of the sentiment classifier for irony, we first implement a one-layer LSTM network trained with the dataset for the sentiment analysis of common twitters to classify the non-ironies into positive and negative non-ironies. In this section, we describe some additional experiments on the transformation from ironic sentences to non-ironic sentences.
Question: What experiments are conducted?
Solution:
Irony Classifier Sentiment Classifier for Irony Sentiment Classifier for Non-irony transformation from ironic sentences to non-ironic sentences