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.

Input: Consider Input: 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?

Output: 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.

Input: Consider Input: ACL-ARC is a dataset of citation intents released by BIBREF7 . The dataset is based on a sample of papers from the ACL Anthology Reference Corpus BIBREF15 and includes 1,941 citation instances from 186 papers and is annotated by domain experts in the NLP field.
Question: What is the size of ACL-ARC datasets?

Output: includes 1,941 citation instances from 186 papers

Input: Consider Input: We adapted BERTNLU from ConvLab-2. We implemented a rule-based model (RuleDST) and adapted TRADE (Transferable Dialogue State Generator) BIBREF19 in this experiment. We adapted a vanilla policy trained in a supervised fashion from ConvLab-2 (SL policy).
Question: What are the benchmark models?
Output: BERTNLU from ConvLab-2 a rule-based model (RuleDST) TRADE (Transferable Dialogue State Generator) a vanilla policy trained in a supervised fashion from ConvLab-2 (SL policy)