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.

[Q]: 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.
[A]: How do they extract topics?


[Q]: A baseline model for automatic extraction of anglicisms was created using the annotated corpus we just presented as training material. As mentioned in Section 3, the task of detecting anglicisms can be approached as a sequence labeling problem where only certain spans of texts will be labeled as anglicism (in a similar way to an NER task). The chosen model was conditional random field model (CRF), which was also the most popular model in both Shared Tasks on Language Identification for Code-Switched Data BIBREF23, BIBREF24.
[A]: Does the paper motivate the use of CRF as the baseline model?


[Q]: We thus adapt an off-the-shelf reward learning algorithm BIBREF7 to the supervised setting for automated data manipulation.
[A]:
What off-the-shelf reward learning algorithm from RL for joint data manipulation learning and model training is adapted?