In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

Input: Consider Input: The current state-of-the-art approach BIBREF14 , BIBREF15 uses maximum entropy and CRF models with a combination of language model and hand-crafted features to predict if each character in the hashtag is the beginning of a new word.

Output: What current state of the art method was used for comparison?

Input: Consider Input: We use the morphological tagging datasets provided by the Universal Dependencies (UD) treebanks (the concatenation of the $4^ ext{th}$ and $6^ ext{th}$ columns of the file format) BIBREF13 .

Output: On which dataset is the experiment conducted?

Input: Consider Input: (1) AutoJudge consistently and significantly outperforms all the baselines, including RC models and other neural text classification models, which shows the effectiveness and robustness of our model. (2) RC models achieve better performance than most text classification models (excluding GRU+Attention), which indicates that reading mechanism is a better way to integrate information from heterogeneous yet complementary inputs. (3) Comparing with conventional RC models, AutoJudge achieves significant improvement with the consideration of additional law articles.
Output: what are their results on the constructed dataset?