instruction:
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.
question:
word embedding, input encoder, alignment, aggregation, and prediction
answer:
what is the architecture of the baseline model?


question:
Notably, automatic translations of TED talks contain more words than the corresponding reference translation, which means that machine-translated texts of this type have also more potential tokens to enter in a coreference relation, and potentially indicating a shining through effect. The same does not happen with the news test set. We see how NMT translations increase the number of mentions about $30\%$ with respect to human references showing even a more marked explicitation effect than human translations do.
answer:
What translationese effects are seen in the analysis?


question:
Sentiment: For each cluster, its overall sentiment score is quantified by the mean of the sentiment scores among all tweets.
answer:

How is sentiment polarity measured?