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?
Example solution: No
Example explanation: Based on the context, the dataset is constructed from a famous Chinese social media microblogging service Weibo.

Problem: We chose three different datasets for the experiments: SNLI, MultiNLI and SICK. The Stanford Natural Language Inference (SNLI) corpus BIBREF4 is a dataset of 570k human-written sentence pairs manually labeled with the labels entailment, contradiction, and neutral. The Multi-Genre Natural Language Inference (MultiNLI) corpus BIBREF5 consisting of 433k human-written sentence pairs labeled with entailment, contradiction and neutral. SICK BIBREF6 is a dataset that was originally constructed to test compositional distributional semantics (DS) models.
Question: Which datasets were used?
Solution: SNLI, MultiNLI and SICK