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.

[Q]: Further, FSDM has a new module called response slot binary classifier that adds extra supervision to generate the slots that will be present in the response more precisely before generating the final textual agent response (see Section "Methodology" for details).
[A]: How do slot binary classifiers improve performance?

[Q]: Specifically, Roget's Thesaurus BIBREF38 , BIBREF39 is used to derive the concepts and concept word-groups to be used as the external lexical resource for our proposed method. By using the concept word-groups, we have trained the GloVe algorithm with the proposed modification given in Section SECREF4 on a snapshot of English Wikipedia measuring 8GB in size, with the stop-words filtered out.
[A]: Do they report results only on English data?

[Q]: The evaluation of chatbots remains an open problem in the field. Recent work BIBREF25 has shown that the automatic evaluation metrics borrowed from machine translation such as BLEU score BIBREF26 tend to align poorly with human judgement. Therefore, in this paper, we mainly adopt human evaluation, along with perplexity, following the existing work.
[A]:
Is some other metrics other then perplexity measured?