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.
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Question: The functional dissimilarity score was computed using sentences from the test set in CoNLL 2017 Universal Dependencies task BIBREF20 for the relevant languages with the provided UPOS sequences. We computed the nearest neighbours experiment for all languages in the training data for the above models.
Question: Which evaluation metrics do they use for language modelling?

Answer: functional dissimilarity score nearest neighbours experiment

Question: Through our experiments, we make subtle points related to: (a) the performance of our features, (b) how our approach compares against human ability to detect drunk-texting, (c) most discriminative stylistic features, and (d) an error analysis that points to future work.
Question: Do the authors mention any confounds to their study?

Answer: No

Question: Slot Filling is an information extraction task which has become popular in the last years BIBREF3 . The task aims at extracting information about persons, organizations or geo-political entities from a large collection of news, web and discussion forum documents.
Question: What is the task of slot filling?

Answer:
The task aims at extracting information about persons, organizations or geo-political entities from a large collection of news, web and discussion forum documents.