instruction:
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
question:
We evaluate our method on IWSLT16 German-English (DE-EN) translation in both directions, WMT15 English-German (EN-DE) translation in both directions, and NIST Chinese-to-English (ZH$
ightarrow $EN) translation.
Question: Which languages do they experiment on?
answer:
German English Chinese

question:
Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs.
Question: How big is QA-CTS task dataset?
answer:
17,833 sentences, 826,987 characters and 2,714 question-answer pairs

question:
To compare fairly, we train and evaluate these systems on the VLSP corpora. In particular, we conduct experiments on Viet Treebank corpus for POS tagging and chunking tasks, and on VLSP shared task 2016 corpus for NER task. All of these corpora are converted to CoNLL format. The corpus of POS tagging task consists of two columns namely word, and POS tag. For chunking task, there are three columns namely word, POS tag, and chunk in the corpus. The corpus of NER task consists of four columns. The order of these columns are word, POS tag, chunk, and named entity. While NER corpus has been separated into training and testing parts, the POS tagging and chunking data sets are not previously divided. For this reason, we use INLINEFORM0 of these data sets as a training set, and the remaining as a testing set. Because our system adopts early stopping method, we use INLINEFORM1 of these data sets from the training set as a development set when training NNVLP system. Table TABREF24 and Table TABREF25 shows the statistics of each corpus.
Question: What datasets do they use for the tasks?
answer:
Viet Treebank corpus for POS tagging and chunking tasks, and on VLSP shared task 2016 corpus for NER task