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:
Results on all sets show an important difference between precision and recall, precision being significantly higher than recall. There is also a significant difference between the results obtained on development and test set (F1 = 89.60, F1 = 87.82) and the results on the supplemental test set (F1 = 71.49). The time difference between the supplemental test set and the development and test set (the headlines from the the supplemental test set being from a different time period to the training set) can probably explain these differences.
answer:
What is the performance of the CRF model on the task described?


question:
Note that GANE-AP delivers better results compared with GANE-OT, suggesting the attention parsing mechanism can further improve the low-level mutual attention matrix.
answer:
Which of their proposed attention methods works better overall?


question:
In general, the model transforms the segments into object-pairs by the TC-CNN and passes sentence through bi-GRU to obtain the global representation. Then we integrate object-pairs with global representation and make a pair-wise inference to detect the relationship among the segments. Ablation studies show that the proposed SCRN at the segment level has the capacity for relational reasoning and promotes the result significantly.
answer:

How is Relation network used to infer causality at segment level?