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:
After removing 135 questions during the validation, the final dataset comprises 13,939 questions, 3,827 of which require commonsense knowledge (i.e. 27.4%).
Question: what is the size of their dataset?
answer:
13,939

question:
Our training data consists of 2.09M sentence pairs extracted from LDC corpus. To test different approaches on Chinese-to-English translation task, we use NIST 2003(MT03) dataset as the validation set, and NIST 2004(MT04), NIST 2005(MT05), NIST 2006(MT06) datasets as our test sets. For English-to-Chinese translation task, we also use NIST 2003(MT03) dataset as the validation set, and NIST 2008(MT08) will be used as test set.
Question: What dataset did they use?
answer:
LDC corpus NIST 2003(MT03) NIST 2004(MT04), NIST 2005(MT05), NIST 2006(MT06) NIST 2008(MT08)

question:
Another widely used method for classifying misleading content is to use distant annotations, for example to classify a tweet based on the domain of a URL that is shared by the tweet, or a hashtag that is contained in the tweet BIBREF6, BIBREF7, BIBREF8. In order to scale annotations that go beyond metadata to larger datasets, Natural Language Processing (NLP) models can be used to automatically label text content.
Question: What proxies for data annotation were used in previous datasets?
answer:
widely used method for classifying misleading content is to use distant annotations, for example to classify a tweet based on the domain of a URL that is shared by the tweet, or a hashtag that is contained in the tweet Natural Language Processing (NLP) models can be used to automatically label text content