西安交通大学Natural Language ProcessingwithDeepLearningXIANHAOTONGUNIVERSITYQuestion Answering交通大学ChenLi2023
Chen Li 2023 Question Answering Natural Language Processing with Deep Learning
Outlines1.Motivation/History2.TheSQuADdataset3.TheStanfordAttentiveReadermodel4.BiDAF5.Recent,moreadvanced architectures
Outlines 1. Motivation/History 2. The SQuAD dataset 3. The Stanford Attentive Reader model 4. BiDAF 5. Recent, more advanced architectures
Outlines1.Motivation/History2.TheSQuADdataset3.TheStanfordAttentiveReadermodel4.BiDAF5.Recent,moreadvancedarchitectures
Outlines 1. Motivation/History 2. The SQuAD dataset 3. The Stanford Attentive Reader model 4. BiDAF 5. Recent, more advanced architectures
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Motivation
MotivationQuestionansweringWith massive collections of full-text documents, i.e., the web, simplyreturning relevant documents is of limited useRather,weoftenwantanswerstoourquestionsEspeciallyonmobileOr using a digital assistant device, like Alexa, Google Assistant, We canfactorthis into two parts:1.Finding documentsthat (might)containananswerWhichcanbehandledbytraditionalinformationretrieval/websearch2.Findingan answerin a paragraph or adocumentThisproblemisoftentermedReadingComprehension.It iswhatwewill focusontoday
Motivation • With massive collections of full-text documents, i.e., the web , simply returning relevant documents is of limited use • Rather, we often want answers to our questions • Especially on mobile • Or using a digital assistant device, like Alexa, Google Assistant, . • We can factor this into two parts: 1. Finding documents that (might) contain an answer • Which can be handled by traditional information retrieval/web search 2. Finding an answer in a paragraph or a document • This problem is often termed Reading Comprehension • It is what we will focus on today l Question answering