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.

Ex Input:
The final embedding dimensionality is equal to the number of unique word labels in the training set, which is 1061.

Ex Output:
Which dimensionality do they use for their embeddings?


Ex Input:
As can be seen, the proposed DSC loss outperforms the best baseline results by a large margin, i.e., outperforming BERT-tagger by +1.86 in terms of F1 score on CTB5, +1.80 on CTB6 and +2.19 on UD1.4.

Ex Output:
What are method's improvements of F1 w.r.t. baseline BERT tagger for Chinese POS datasets?


Ex Input:
Different from scaled dot-product attention, Gaussian-masked directional attention expects to pay attention to the adjacent characters of each positions and cast the localness relationship between characters as a fix Gaussian weight for attention. We assume that the Gaussian weight only relys on the distance between characters.

Ex Output:

How does Gaussian-masked directional multi-head attention works?