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.

Input: Consider Input: In its general form, a type-logical grammar consists of following components:
Question: Does Grail accept Prolog inputs?

Output: No

Input: Consider Input: We use standard Precision, Recall and F1 at mention level (Micro) and at the document level (Macro) as measurements. We can see the average linking precision (Micro) of WW is lower than that of TAC2010, and NCEL outperforms all baseline methods in both easy and hard cases.
Question: How do they verify generalization ability?

Output: By calculating Macro F1 metric at the document level.

Input: Consider Input: For model distillation BIBREF6 , we extract sentences from Wikipedia in languages for which public multilingual is pretrained. For each sentence, we use the open-source BERT wordpiece tokenizer BIBREF4 , BIBREF1 and compute cross-entropy loss for each wordpiece: INLINEFORM0

where INLINEFORM0 is the cross-entropy function, INLINEFORM1 is the softmax function, INLINEFORM2 is the BERT model's logit of the current wordpiece, INLINEFORM3 is the small BERT model's logits and INLINEFORM4 is a temperature hyperparameter, explained in Section SECREF11 .

To train the distilled multilingual model mMiniBERT, we first use the distillation loss above to train the student from scratch using the teacher's logits on unlabeled data. Afterwards, we finetune the student model on the labeled data the teacher is trained on.
Question: How do they compress the model?
Output: we extract sentences from Wikipedia in languages for which public multilingual is pretrained. For each sentence, we use the open-source BERT wordpiece tokenizer BIBREF4 , BIBREF1 and compute cross-entropy loss for each wordpiece: INLINEFORM0