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.

[Q]: For de-identification tasks, the three metrics we will use to evaluate the performance of our architecture are Precision, Recall and INLINEFORM0 score as defined below. 
 Question: What evaluation metrics do they use?
[A]: Precision, Recall and INLINEFORM0 score


[Q]: We introduce a surrogate training objective that avoids these problems and as a result is fully continuous.  Specifically, we form a continuous function softLB that seeks to approximate the result of running our decoder on input INLINEFORM0 and then evaluating the result against INLINEFORM1 using INLINEFORM2 . 
 Question: Do they provide a framework for building a sub-differentiable for any final loss metric?
[A]: Yes


[Q]: Three annotators, who have no knowledge about which system the response is from, are then required to evaluate among win (response$_1$ is better), loss (response$_2$ is better) and tie (they are equally good or bad) independently, considering four aspects: coherence, logical consistency, fluency and diversity. 
 Question: What human judgement metrics are used?
[A]:
coherence, logical consistency, fluency and diversity