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.

Q: We used dataset, HEOT obtained from one of the past studies done by Mathur et al. where they annotated a set of cleaned tweets obtained from twitter for the conversations happening in Indian subcontinent. A labelled dataset for a corresponding english tweets were also obtained from a study conducted by Davidson et al. 
 Question: How is the dataset collected?

A: A labelled dataset for a corresponding english tweets were also obtained from a study conducted by Davidson et al HEOT obtained from one of the past studies done by Mathur et al
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Q: To understand the reason behind the drop in performance, first we need to understand how BERT processes input text data. BERT uses WordPiece tokenizer to tokenize the text. WordPiece tokenizer utterances based on the longest prefix matching algorithm to generate tokens . The tokens thus obtained are fed as input of the BERT model. When it comes to tokenizing noisy data, we see a very interesting behaviour from WordPiece tokenizer. Owing to the spelling mistakes, these words are not directly found in BERT’s dictionary. Hence WordPiece tokenizer tokenizes noisy words into subwords. However, it ends up breaking them into subwords whose meaning can be very different from the meaning of the original word. Often, this changes the meaning of the sentence completely, therefore leading to substantial dip in the performance. 
 Question: What is the reason behind the drop in performance using BERT for some popular task?

A: Hence WordPiece tokenizer tokenizes noisy words into subwords. However, it ends up breaking them into subwords whose meaning can be very different from the meaning of the original word. Often, this changes the meaning of the sentence completely, therefore leading to substantial dip in the performance.
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Q: The SemEval-2010 benchmark dataset BIBREF0 is composed of 244 scientific articles collected from the ACM Digital Library (conference and workshop papers). 
 Question: how many articles are in the dataset?

A:
244
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