Lecture 16 addresses the question ""Can all NLP tasks be seen as question answering problems?"". A word embedding is a learned representation for text where words that have the same meaning have a similar representation. The simplest implementations would pass the top n most relevant documents to the document reader for answer extraction but this, too, can be made more sophisticated by breaking documents into their respective passages or paragraphs and filtering them (based on named entity matching or answer type, for example) to narrow down the number of passages sent to the document reader. In the question-processing phase a number of pieces of information from the question are extracted. Built in the 1960s, it was limited to answering questions surrounding one year’s worth of baseball facts and statistics. Machines do not inherently understand human languages any more than the average human understands machine language. For example, a QA system with knowledge of a company’s FAQs can streamline customer experience, while QA systems built atop internal company documentation could provide employees easier access to logs, reports, financial statements, or design docs. Modern reading comprehension algorithms come in two broad flavors: feature-based and neural-based. These candidate answers can either be extracted from text documents or from structured knowledge bases. Thus RNN came into existence, which solved this issue with the help of a Hidden Layer. QA algorithms have been developed to harness the information from either paradigm: knowledge-based systems for structured data and information retrieval-based systems for unstructured (text) data. Question Answering is a human-machine interaction to extract information from data using natural language queries. Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning This recent paper proposes a deep learning model to translate natural language questions to structured SQL queries. It supplies a set of candidate documents that could answer the question (often with mixed results, per the Google search shown above). Information retrieval-based question answering (IR QA) systems find and extract a text segment from a large collection of documents. • Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning. Question answering systems are being heavily researched at the moment thanks to huge advancements gained in the Natural Language Processing field. The search results below the snippet illustrate some of the reasons why an IR QA system can be more useful than a search engine alone. useful context to make decisions for those who might build their own QA system, existing QA training sets for Transformers and what you’ll need to develop your own, how to evaluate the quality of a QA system - both the reader and retriever, building a search engine over a large set of documents. analytics. In this section, we’ll highlight some of the most widely used techniques in each data regime - concentrating more on those for unstructured data, since this will be the focus of our applied research. An NLP algorithm can match a user’s query to your question bank and automatically present the most relevant answer. Without the snippet box at the top, a user would have to skim each of these links to locate their answer - with varying degrees of success. Google recently explained how they are using state-of-the-art NLP to enhance some of their search results. Unlike standard feedforward neural networks, LSTM has feedback connections. It is only recently that with the introduction of memory and attention based architectures there has been some progress in this field. Question answering is not a new research area Question answering systems can be found in many areas of NLP research, including: Natural language database systems A lot of early NLP work on these Spoken dialog systems Currently very active and commercially relevant The focus on open-domain QA is new MURAX (Kupiec1993): Encyclopediaanswers The field of QA is just starting to become commercially viable and it’s picking up speed. Some QA systems exploit a hybrid design that harvests information from both data types; IBM’s Watson is a famous example. 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