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]: In this section, we present the experimental setting for this task

Corpus: SEAME (South East Asia Mandarin-English), a conversational Mandarin-English code-switching speech corpus consists of spontaneously spoken interviews and conversations BIBREF8 . 
[A]: What languages are explored in the work?


[Q]: To capture this interesting property, we propose a new tagging scheme consisting of three tags, namely { INLINEFORM0 }.

INLINEFORM0 tag indicates that the current word appears before the pun in the given context.

INLINEFORM0 tag highlights the current word is a pun.

INLINEFORM0 tag indicates that the current word appears after the pun.
[A]: What is the tagging scheme employed?


[Q]: In addition, we propose a new standard experimental protocol for the IBM-UB-1 dataset BIBREF25 (Sec. SECREF50 ) to enable easier comparison between approaches in the future. The IAM-OnDB dataset BIBREF42 is probably the most used evaluation dataset for online handwriting recognition. It consists of 298 523 characters in 86 272 word instances from a dictionary of 11 059 words written by 221 writers. We use the standard IAM-OnDB dataset separation: one training set, two validations sets and a test set containing 5 363, 1 438, 1 518 and 3 859 written lines, respectively. We tune the decoder weights using the validation set with 1 438 items and report error rates on the test set. We provide an evaluation of our production system trained on our in-house datasets applied to a number of publicly available benchmark datasets from the literature. Note that for all experiments presented in this section we evaluate our current live system without any tuning specifec to the tasks at hand.

The ICDAR-2013 Competition for Online Handwriting Chinese Character Recognition BIBREF45 introduced a dataset for classifying the most common Chinese characters. We report the error rates in comparison to published results from the competition and more recent work done by others in Table TABREF56 . In the ICFHR2018 Competition on Vietnamese Online Handwritten Text Recognition using VNOnDB BIBREF50 , our production system was evaluated against other systems. The system used in the competition is the one reported and described in this paper. Due to licensing restrictions we were unable to do any experiments on the competition training data, or specific tuning for the competition, which was not the case for the other systems mentioned here.
[A]:
What datasets did they use?