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]: The input document/summary that may have unordered sentences is processed so that it will have sentences clustered together. To test our approach, we jumble the ordering of sentences in a document, process the unordered document and compare the similarity of the output document with the original document. To structure an unordered document is an essential task in many applications. It is a post-requisite for applications like multiple document extractive text summarization where we have to present a summary of multiple documents. It is a prerequisite for applications like question answering from multiple documents where we have to present an answer by processing multiple documents.
Question: What is an unordered text document, do these arise in real-world corpora?
[A]: A unordered text document is one where sentences in the document are disordered or jumbled. It doesn't appear that unordered text documents appear in corpora, but rather are introduced as part of processing pipeline.

[Q]: Experiments on two publicly available datasets (Camrest BIBREF11 and InCar Assistant BIBREF6) confirm the effectiveness of the KB-retriever.
Question: Which dialog datasets did they experiment with?
[A]: Camrest InCar Assistant

[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.