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.

Table TABREF46 shows that our Open model achieves more than 3 points of f1-score than the state-of-the-art result, and RelAwe with DepPath&RelPath achieves the best in both Closed and Open settings.
How big is improvement over the old  state-of-the-art performance on CoNLL-2009 dataset?

The best baseline model is NO-MOVE, reaching an accuracy of 30.3% on single sentences and 0.3 on complete paragraphs.
How well did the baseline perform?

 We defined a sequence labeling task to extract custom entities from user input. We assumed seven (7) possible entities (see Table TABREF43) to be recognized by the model: topic, subtopic, examination mode and level, question number, intent, as well as the entity other for remaining words in the utterance.   We defined a classification problem to predict the system's next action according to the given user input. We assumed 13 custom actions (see Table TABREF42) that we considered being our labels. In the conversational dataset, each input was automatically labeled by the rule-based system with the corresponding next action and the dialogue-id. Thus, no additional post-labeling was required. 

How does the IPA label data after interacting with users?