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: 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
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Q: Following lebret2016neural, we used BLEU-4, NIST-4 and ROUGE-4 as the evaluation metrics.
Question: What metrics are used for evaluation?

A: BLEU-4 NIST-4 ROUGE-4
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Q: Word embeddings have risen in popularity for NLP applications due to the success of models designed specifically for the big data setting. Note that "big" datasets are not always available, particularly in computational social science NLP applications, where the data of interest are often not obtained from large scale sources such as the internet and social media, but from sources such as press releases BIBREF11 , academic journals BIBREF10 , books BIBREF12 , and transcripts of recorded speech BIBREF13 , BIBREF14 , BIBREF15 have proposed a model-based method for training interpretable corpus-specific word embeddings for computational social science, using mixed membership representations, Metropolis-Hastings-Walker sampling, and NCE. Experimental results for prediction, supervised learning, and case studies on state of the Union addresses and NIPS articles, indicate that high-quality embeddings and topics can be obtained using the method. The results highlight the fact that big data is not always best, as domain-specific data can be very valuable, even when it is small.
Question: Why is big data not appropriate for this task?

A:
Training embeddings from small-corpora can increase the performance of some tasks
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