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: In this section, we consider two kinds of relational classification tasks: (1) relation prediction and (2) relation extraction. We hope to design a simple and clear experiment setup to conduct error analysis for relational prediction. Therefore, we consider a typical method TransE BIBREF3 as the subject as well as FB15K BIBREF3 as the dataset. For relation extraction, we consider the supervised relation extraction setting and TACRED dataset BIBREF10 . As for the subject model, we use the best model on TACRED dataset position-aware neural sequence model.
Question: Which competitive relational classification models do they test?
Solution: For relation prediction they test TransE and for relation extraction they test position aware neural sequence model