Question: What did they do with their three data sets in experiments?
Evidence: Three datasets are used in the experiments as described in the following subsections. We use two datasets for Thai sentence segmentation, and the third dataset is used for English punctuation restoration. The statistics of the preprocessed data are shown in Table TABREF31 , including the number of sequences and the number of vocabulary words in each dataset. We also calculate the average number of words per passage in the unlabeled data that do not appear in the labeled data, as shown in Table TABREF32 .
This dataset is a Thai part-of-speech-tagged dataset containing 10,864 sentences. In the corpus, text was separated into paragraphs, sentences, and words hierarchically by linguists. Each word was also manually assigned a POS by linguists. These data include no unlabeled data with the same word segmentation and POS tag set. Hence, we do not execute CVT on this dataset.
Our data preprocessing on the ORCHID corpus was similar to that in BIBREF8 : all the comments are removed, and the data are partitioned into 10 parts containing equal numbers of sentences to support 10-fold cross-validation. Each training set is split into one part used for validation and the rest is used for model training. Subsequently, all the words in each dataset are concatenated and then separated into sequences with 200 words per instance. Each sequence always begins with the first word of a sentence. If a sequence ends with an unfinished sentence, the next sequence starts with that complete sentence.
This Thai dataset includes many types of labeled data useful in sentence segmentation tasks. The raw text was generated by users having conversations in the financial domain and were acquired mainly by crawling social sites. The labeled data for sentence segmentation were manually annotated by linguists using the definitions in BIBREF21 .
At the time of this study, the dataset was extended from that in BIBREF21 ; the data were collected from January 2017 to December 2017. The labeled dataset includes 48,374 passages. To support semi-supervised learning, the first 3 months of data (96,777 passages) are unlabeled.
Because the data stem from social media, some text exists that cannot be considered as part of any sentence, such as product links, symbols unrelated to sentences, and space between sentences. These portions were not originally annotated as sentences by the linguists. However, in this work, we treat these portions as individual sentences and tag the first word of each fraction as the sentence boundary.
For evaluation purposes, the collection of passages in this dataset is based on 5-fold cross-validation, similar to the previous work BIBREF21 . The passages are treated as input sequences for the model. For each passage, word segmentation and POS tagging are processed by the custom models from this dataset.
We adopted this English-language dataset to enable comparisons with models intended for other languages. The dataset is composed of TED talk transcripts. To compare our model with those of previous works, we selected the training dataset for the machine translation track in IWSLT2012 and separated it into training and validation sets containing 2.1 million and 295 thousand words, respectively. The testing dataset is the IWSLT2011 reference set, which contains 13 thousand words. To acquire unlabeled data for semi-supervised learning, we adopted the IWSLT2016 machine translation track training data; duplicate talks that also appear in IWSLT2012 are discarded.
The data preprocessing follows the process in BIBREF36 . Each sequence is generated from 200 words, of which beginning is always the first word in a sentence. If a sentence is cut at the end of a sequence, that sentence is copied in full to the beginning of the next sequence.
To use our model, the POS of each word is required. However, the IWSLT dataset contains only the raw text of transcripts and does not include POS tags. Thus, we implement POS tagging using a special library BIBREF38 to predict the POS of each word.
Three datasets are used in the experiments as described in the following subsections. We use two datasets for Thai sentence segmentation, and the third dataset is used for English punctuation restoration.
Answer: They use two datasets for Thai sentence segmentation, and the third dataset is used for English punctuation restoration.