instruction:
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.
question:
The overall Total Accuracy score reported in table TABREF19 using the entire feature set is 549. 
answer:
Do they experiment with the dataset?


question:
To evaluate model robustness, we devise a test set consisting of ‘adversarial’ examples, i.e, perturbed examples that can potentially change the base model's prediction. These could stem from paraphrasing a sentence, e.g., lexical and syntactical changes. We use two approaches described in literature: back-translation and noisy sequence autoencoder. Note that these examples resemble black-box attacks but are not intentionally designed to fool the system and hence, we use the term 'adversarial' broadly. We use these techniques to produce many paraphrases and find a subset of utterances that though very similar to the original test set, result in wrong predictions. We will measure the model robustness against such changes.
answer:
How authors create adversarial test set to measure model robustness?


question:
We tried Glove BIBREF2 and Twitter word2vec BIBREF3 code for training embeddings for the processed tweets.
answer:

What embeddings do they use?