Question Answering on Dialogue Sample Clauses

Question Answering on Dialogue. ‌ There have been a number of attempts made in the Natural Language Pro- cessing community on Question Answering challenges with a variety of texts and questions. The specific classifications of the questions will be discussed in Chapter 2.1. The texts on the other hand, vary by size and types. Some data-sets simply provide a knowledge base containing quantities of passages as context, which requires the model to use Information Retrieval models to filter relevant passages to a specific question. Examples of this kind in- cludes Quasar-T (Question Answering by Search and Reading) [4], which is based on Trivia question set with 100 passages collected for each question; Search QA [5], which is based on Jeopardy! question set with 50 web articles collected for each question. Other data-sets provide more specific passages, usually annotated from non-fiction writings or descriptive articles. Examples of this kind include SQuAD (Stanford Question Answering Dataset) [8], which is annotated from Wikipedia articles. Regardless of the corpus size, all of the passages selected by previous work feature non-fictional, descriptive or scholarly writings as relevant context to the questions raised. Very little work has been conducted on Question Answering with dialogue as context. Dialog data is significant, because not only it is available in large quantity, but also the quantity is growing rapidly. There were approximately 21.9 billion online messages sent each day in the year of 2017, an increase of 17% increase from an daily average of 18.7 billion from the year of 2016[10]. This resource only recently became available due to the rapid growth of information technology. The ultimate aim of this project is to collect a robust question answering data-set based on dialogue data as well as to explore ways of attempting the Question Answering challenge on this data set.
Sample 1
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