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]: Table TABREF20 shows that, on WoZ and DSTC2 datasets, SIM model has the same number of parameters, which is only 23% and 19% of that in GLAD model.
Question: How do they measure model size?
[A]: By the number of parameters.

[Q]: We first conduct experiments to compare the performance of FlowSeq with strong baseline models, including NAT w/ Fertility BIBREF6, NAT-IR BIBREF7, NAT-REG BIBREF25, LV NAR BIBREF26, CTC Loss BIBREF27, and CMLM BIBREF8.
Question: What non autoregressive NMT models are used for comparison?
[A]: NAT w/ Fertility NAT-IR NAT-REG LV NAR CTC Loss CMLM

[Q]: In the experiments, we compare model performance between GluonCV/NLP and other open source implementations with Caffe, Caffe2, Theano, and TensorFlow, including ResNet BIBREF8 and MobileNet BIBREF9 for image classification (ImageNet), Faster R-CNN BIBREF10 for object detection (COCO), Mask R-CNN BIBREF11 for instance segmentation, Simple Pose BIBREF12 for pose estimation (COCO), textCNN BIBREF13 for sentiment analysis (TREC), and BERT BIBREF14 for question answering (SQuAD 1.1), sentiment analysis (SST-2), natural langauge inference (MNLI-m), and paraphrasing (MRPC).
Question: Do they experiment with the toolkits?
[A]:
Yes