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.

[EX Q]: To report the result for answer selection, MAP and MRR are used; however, the answer triggering task is evaluated by F1-score. The result for MULT Method is reported in Table. 1.
[EX A]: Is accuracy the only metric they used to compare systems?

[EX 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).
[EX A]: How do slot binary classifiers improve performance?

[EX Q]: Wav2vec BIBREF22 proposed a multi-layer convolutional neural network optimized via a noise contrastive binary classification and was applied to WSJ ASR tasks. Our experiments consisted of three different setups: 1) a fully-supervised system using all labeled data; 2) an SSL system using wav2vec features; 3) an SSL system using our proposed DeCoAR features. All models used were based on deep BLSTMs with the CTC loss criterion.
[EX A]:
What are baseline models on WSJ eval92 and LibriSpeech test-clean?