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.

The improvement when the difficult subset with expert annotations is mixed with the remaining crowd annotation is 3.5 F1 score, much larger than when a random set of expert annotations are added.
How much higher quality is the resulting annotated data?

These sentiment scores were automatically obtained through the topic extraction and sentiment analysis pipeline introduced in our prior work BIBREF15 and pretrained on in-house psychiatric EHR text.
How do they extract topics?

n this work, we apply an automated neural ensemble annotation process for dialogue act labeling. Several neural models are trained with the Switchboard Dialogue Act (SwDA) Corpus BIBREF9, BIBREF10 and used for inferring dialogue acts on the emotion datasets. We ensemble five model output labels by checking majority occurrences (most of the model labels are the same) and ranking confidence values of the models. We adopted the neural architectures based on Bothe et al. bothe2018discourse where two variants are: non-context model (classifying at utterance level) and context model (recognizing the dialogue act of the current utterance given a few preceding utterances).

How many models were used?