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.

Example Input: 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?
Example Output: Yes

Example Input: Just as introduced in sec:introduction, it is intractable to compute similarity exactly, as involving INLINEFORM0 computation. Hence, we consider the monte-carlo approximation: DISPLAYFORM0

where INLINEFORM0 is a list of entity pairs sampled from INLINEFORM1 . We use sequential sampling to gain INLINEFORM6 , which means we first sample INLINEFORM7 given INLINEFORM8 from INLINEFORM9 , and then sample INLINEFORM10 given INLINEFORM11 and INLINEFORM12 from INLINEFORM13 . 
 Question: Which sampling method do they use to approximate similarity between the conditional probability distributions over entity pairs?
Example Output: monte-carlo sequential sampling

Example Input: In this paper, we propose an improved RNN-T model with language bias to alleviate the problem. The model is trained to predict language IDs as well as the subwords. To ensure the model can learn CS information, we add language IDs in the CS point of transcription, as illustrated in Fig. 1. 
 Question: How do they obtain language identities?
Example Output:
model is trained to predict language IDs as well as the subwords we add language IDs in the CS point of transcriptio