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: This section reports the results of the experiments conducted on two data sets for evaluating the performances of wDTW and wTED against other baseline methods. We denote the following distance/similarity measures. WMD: The Word Mover's Distance introduced in Section SECREF1 . VSM: The similarity measure introduced in Section UID12 . PV-DTW: PV-DTW is the same as Algorithm SECREF21 except that the distance between two paragraphs is not based on Algorithm SECREF20 but rather computed as INLINEFORM0 where INLINEFORM1 is the PV embedding of paragraph INLINEFORM2 . PV-TED: PV-TED is the same as Algorithm SECREF23 except that the distance between two paragraphs is not based on Algorithm SECREF20 but rather computed as INLINEFORM0 .

A: Which are the state-of-the-art models?
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Q: Trinomials are likely to appear in exactly one order, and when they appear in more than one order the last word is almost always the same across all instances.

A: Are there any new finding in analasys of trinomials that was not present binomials?
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Q: In order to validate the proposed classification model in real crowdsourcing settings, Amazon Mechanical Turk (AMT) was used to obtain labels from multiple annotators for two popular datasets: Reuters-21578 BIBREF30 and LabelMe BIBREF31 . In order to first validate the proposed model for classification problems in a slightly more controlled environment, the well-known 20-Newsgroups benchmark corpus BIBREF29 was used by simulating multiple annotators with different levels of expertise.

A:
what datasets were used?
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