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:
We evaluate sentence-level semantics using averaged bag of vectors (BoV) representations on the Semantic Textual Similarity (STSB) task BIBREF21 and Word Content (WC) probing task (identify from a list of words which is contained in the sentence representation) from SentEval BIBREF22. Syntax: Similarly, we use the Google Syntactic analogies (GSyn) BIBREF9 to evaluate word-level syntactic information, and Depth (Dep) and Top Constituent (TopC) (of the input sentence's constituent parse tree) probing tasks from SentEval BIBREF22 for sentence-level syntax.
answer:
What semantic and syntactic tasks are used as probes?


question:
 In almost all genres, DenseNMT models are significantly better than the baselines.
answer:
did they outperform previous methods?


question:
In this paper, we use three data sets from the literature to train and evaluate our own classifier. Data collected by BIBREF3 , which we term the Sexist/Racist (SR) data set, was collected using an initial Twitter search followed by analysis and filtering by the authors and their team who identified 17 common phrases, hashtags, and users that were indicative of abusive speech. BIBREF4 collected the HATE dataset by searching for tweets using a lexicon provided by Hatebase.org. The final data set we used, which we call HAR, was collected by BIBREF9 ; we removed all retweets reducing the dataset to 20,000 tweets. Tweets were labeled as “Harrassing” or “Non-Harrassing”; hate speech was not explicitly labeled, but treated as an unlabeled subset of the broader “Harrassing” category BIBREF9 .
answer:

Which publicly available datasets are used?