question answering nlp

Google recently explained how they are using state-of-the-art NLP to enhance some of their search results. Question Answering (QA) is a fast-growing research area that brings together research from Information Retrieval (IR), Information Extraction (IE) and Natural Language Processing (NLP). A contemporary example of closed domain QA systems are those found in some BI applications. This article will present key ideas about creating and coding a question answering system based on a neural network. Question answering. It has been developed by Boris Katz and his associates of the InfoLab Group at the MIT Computer Science and Artificial Intelligence Laboratory. While this is an exciting development, it does have its drawbacks. Systems for mapping from a text string to any logical form are called semantic parsers. These systems can even answer general trivia. Two of the earliest QA systems, BASEBALL and LUNAR were successful due to their core database or knowledge system. In this paper, a discussion about various approaches starting from the basic NLP and algorithms based approach has been done and the paper eventually builds towards the recently proposed methods of Deep Learning. Semantic parsers for question answering usually map either to some version of predicate calculus or a query language like SQL or SPARQL. The evaluation of the proposed models was done on twenty tasks of babI dataset of Facebook. IR QA systems are not just search engines, which take general natural language terms and provide a list of relevant documents. The merging and ranking is actually run iteratively; first the candidates are ranked by the classifier, giving a rough first value for each candidate answer, then that value is used to decide which of the variants of a name to select as the merged answer, then the merged answers are re-ranked. • Now that we’ve covered some background, we can describe our approach. Lecture 16 addresses the question ""Can all NLP tasks be seen as question answering problems?"". The vast majority of all QA systems answer factual questions: those that start with who, what, where, when, and how many. Before moving to this we firstly understand about word embeddings. Question answering systems are being heavily researched at the moment thanks to huge advancements gained in the Natural Language Processing field. Unlike standard feedforward neural networks, LSTM has feedback connections. The START Natural Language Question Answering System START, the world's first Web-based question answering system, has been on-line and continuously operating since December, 1993. With 100,000+ question-answer pairs on 500+ articles, SQuAD is significantly larger than previous reading comprehension datasets. In this blog, I want to cover the main building blocks of a question answering model. 16 min read, methods We hope this new format suits the above goals and makes the topic more accessible, while ultimately being useful. Question Answering (QA) System is very useful as most of the deep learning related problems can be modeled as a question answering problem. Google’s search engine product adds a form of question answering in addition to its traditional search results, as illustrated here: Google took our question and returned a set of 1.3 million documents (not shown) relevant to the search terms, i.e., documents about Abraham Lincoln. These algorithms search over all documents often using standard tf-idf cosine matching to rank documents by relevance. Parsing sentences into phrases and then deciding the functionality of the phrase … Rather than relying on keywords, these methods use extensive datasets that allow the model to learn semantic embeddings for the question and the passage. The query specifies the keywords that should be used for the IR system to use in searching for documents. Thus, the NLP technology focuses on to build language-based responses that can be given to humans when they ask questions. Answer Type Detection Question Document and Passsage Retrieval passages DocumentDocument Document Question Classification Parsing Named Entity Tagging Relation Extraction Coreference From Structured Data Relation Retrieval DBPedia Freebase (2) Candidate Answer Generation Candidate Answer Candidate Answer Candidate CandidateAnswer Answer Candidate Answer Candidate AnswerCandidate Answer … These candidate answers can either be extracted from text documents or from structured knowledge bases. The database can be a full relational database, or simpler structured databases like sets of RDF triples. The techniques and methods developed from question answering inspire new ideas in many closely related areas such as document retrieval, time and named-entity recognition (NER), etc. When the model doesn’t work, it’s not always straightforward to identify the problem - and scaling these models is still a challenging prospect. Question-Answering systems (QA) were developed in the early 1960s. The main and most important feature of RNN is Hidden state, which remembers some information about a sequence. NLP-progress / chinese / question_answering.md Go to file Go to file T; Go to line L; Copy path Cannot retrieve contributors at this time. A well-developed QA system bridges the gap between the two, allowing humans to extract knowledge from data in a way that is natural to us, i.e., asking questions. The answer type is categorical, e.g., person, location, time, etc. By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summarize text, and even built a chatbot! Relative insensitivity to gap length is an advantage of LSTM over RNNs, hidden Markov models and other sequence learning methods in numerous applications, EEoI for Efficient ML with Edge Computing, Modular image processing pipeline using OpenCV and Python generators, Attention in end-to-end Automatic Speech Recognition, Introduction and a detailed explanation of the k Nearest Neighbors Algorithm, WTF is Wrong With My Model? Because we’ll be discussing explicit methods and techniques, the following sections are more technical. One of the most important is the lexical answer type. There’s more than one way to cuddle a cat, as the saying goes. Google’s QA capability as demonstrated above would also be considered open domain. When these things happen, we’ll share our thoughts on what worked, what didn’t, and why - but it’s important to note upfront that while we do have a solid goal in mind, the end product may turn out to be quite different than what we currently envision. Implementation details and various tweaks in the algorithms that produced better results have also been discussed. Key players in the industry have developed incredibly advanced models, some of which are already performing at human level. This goes beyond the standard capabilities of a search engine, which typically only return a list of relevant documents or websites. Feature-based answer extraction can include rule-based templates, regex pattern matching, or a suite of NLP models (such as parts-of-speech tagging and named entity recognition) designed to identify features that will allow a supervised learning algorithm to determine whether a span of text contains the answer. LSTMs were developed to deal with the exploding and vanishing gradient problems that can be encountered when training traditional RNNs. And that’s precisely why we wanted to invite you along for the journey! Once you’ve decided the scope of knowledge your QA system will cover, you must also determine what types of questions it can answer. Information retrieval-based question answering (IR QA) systems find and extract a text segment from a large collection of documents. How a QA system is designed depends, in large part, on three key elements: the knowledge provided to the system, the types of questions it can answer, and the structure of the data supporting the system. At the beginning of this article, we said we were going to build a QA system. IR QA systems perform an additional layer of processing on the most relevant documents to deliver a pointed answer, based on the contents of those documents (like the snippet box). b) Knowledge-based question answering is the idea of answering a natural language question by mapping it to a query over a structured database. The field of QA is just starting to become commercially viable and it’s picking up speed. We’ll share what we learn each step of the way by posting and discussing example code, in addition to articles covering topics like: Because we’ll be writing about our work as we go, we might end up in some dead ends or run into some nasty bugs; such is the nature of research! 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. We’ll revisit this example in a later section and discuss how this technology works in practice and how we can (and will!) The problem of making a fully functional question answering system is one problem which has been quite popular among researchers. One useful feature is the answer type identified by the document retriever during query processing. We like jokes). The success of these systems will vary based on the use case, implementation, and richness of data. An NLP algorithm can match a user’s query to your question bank and automatically present the most relevant answer. Contemporary IR QA systems first identify the most relevant documents in the collection, and then extract the answer from the contents of those documents. The domain represents the embodiment of all the knowledge the system can know. So how does this technology work? 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. Let’s dive deeper into each of these components. These systems generally have two main components: the document retriever and the document reader. The document reader consists of reading comprehension algorithms built with core NLP techniques. background. Jun 9, 2020 • 31 min read no answer null threshold bert distilbert exact match F1 robust predictions. analytics as one of the top trends poised to make a substantial impact in the next three to five years. However, research is emerging that would allow QA systems to answer hypothetical questions, cause-effect questions, confirmation (yes/no) questions, and inferential questions (questions whose answers can be inferred from one or more pieces of evidence). These models generally perform better (according to your quantitative metric of choice) relative to the number of parameters they have (the more, the better), but the cost of inference also goes up - and with it, the difficulty of implementation in settings like federated learning scenarios or on mobile devices. These systems can be made more robust by providing lexicons that capture the semantics and variations of natural language. Haystack enables Question Answering at Scale. (For a detailed dive into these architectures, interested readers should check out these excellent posts for Seq2Seq and Transformers.) For instance, in our employee database example, a question might contain the word “employed” rather than “hired,” but the intention is the same. 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. In the question-processing phase a number of pieces of information from the question are extracted. QA systems can augment this existing technology, providing a deeper understanding to improve user experience. For example, an employee database might have a start-date template consisting of handwritten rules that search for when and hired since “when was Employee Name hired” would likely be a common query. As explained above, question answering systems process natural language queries and output concise answers. One of the key ways that ML is augmenting BI platforms is through the incorporation of natural language query functionality, which allows users to more easily query systems, and retrieve and visualize insights in a natural and user-friendly way, reducing the need for deep expertise in query languages, such as SQL. The IR query is then passed to an IR algorithm. Abstract Painting by Steve Johnson on Unsplash. The DeepQA system runs parsing, named entity tagging, and relation extraction on the question. NLP helps the system to identify and understand the meaning of any sentences with proper contexts. One best example of such problems is the question answering problem. In traditional neural networks, all the inputs and outputs are independent of each other, but in cases like when it is required to predict the next word of a sentence, the previous words are required and hence there is a need to remember the previous words. In the final answer merging and scoring step, it first merges the candidate answers that are equivalent. So previously you've seen the transformer decoder and now you're going to look at the transformer encoder so it's very similar. … Next DeepQA extracts the question focus. LSTM networks are well-suited to classifying, processing and making predictions based on time series data, since there can be lags of unknown duration between important events in a time series. Question Answering is the task of answering questions (typically reading comprehension questions), but abstaining when presented with a question that cannot be answered based on the provided context ( Image credit: SQuAD) Below we illustrate the workflow of a generic IR-based QA system. Question answering (QA) is a computer science discipline within the fields of information retrieval and natural language processing (NLP), which is concerned with building systems that automatically answer questions posed by humans in a natural language. A word embedding is a learned representation for text where words that have the same meaning have a similar representation. This general capability can be implemented in dozens of ways. The Transformer architecture in particular is currently revolutionizing the entire field of NLP. Other features could include the number of matched keywords in the question, the distance between the candidate answer and the query keywords, and the location of punctuation around the candidate answer. While we won’t hazard a guess at exactly how Google extracted “gray” from these search results, we can examine how an IR QA system could exhibit similar functionality in a real world (e.g., non-Google) implementation. The collection can be as vast as the entire web (open domain) or as specific as a company’s Confluence documents (closed domain). Sophisticated Google searches with precise answers are fun, but how useful are QA systems in general? Models builts on this architecture include BERT (and its myriad off-shoots: RoBERTa, ALBERT, distilBERT, etc. Learnt a whole bunch of new things. With 100,000+ question-answer pairs on 500+ articles, SQuAD is significantly larger than previous reading comprehension datasets. Next is the candidate answer generation stage according to the question type, where the processed question is combined with external documents and other knowledge sources to suggest many candidate answers. Recurrent neural network are a type of Neural Network where the output from previous step are fed as input to the current step. Neural network models that perform well in this arena are Seq2Seq models and Transformers. Machine Learning . Get Started. Generally, their domain is scoped to whatever data the user supplies, so they can only answer questions on the specific datasets to which they have access. We already talked about how the snippet box acts like a QA system. Gartner recently identified natural language processing and conversational There has been a rapid progress on the SQuAD dataset with some of the latest models achieving human level acc… Business Intelligence (BI) platforms are beginning to use Machine Learning (ML) to assist their users in exploring and analyzing their data through ML-augmented data preparation and insight generation. 6 min read. Question Answering models do exactly what the name suggests: given a paragraph of text and a question, the model looks for the answer in the paragraph. Stay tuned; in our next post we’ll start digging into the nuts and bolts! Create a Question Answering Machine Learning model system which will take comprehension and questions as input, process the comprehension and prepare answers from it.With the Concept of Natural Language Processing, we can achieve this objective. Neural-based reading comprehension approaches capitalize on the idea that the question and the answer are semantically similar. At Cloudera Fast Forward, we routinely report on the latest and greatest in machine learning capabilities. LSTM model is used in this question answering system. challenge in 2011 is an example of a system that relies on a wide variety of resources to answer questions. Neural Question Answering at Scale . And we’ll note that, while we provide an overview here, an even more comprehensive discussion can be found in the Question Answering chapter of Jurafsky and Martin’s Speech and Language Processing (a highly accessible textbook). Google also used what it knows about the contents of some of those documents to provide a “snippet” that answered our question in one word, presented above a link to the most pertinent website and keyword-highlighted text. build our own QA system. 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 A deep dive into computing QA predictions and when to tell BERT to zip it! The Chinese Machine Reading … Question Answering is a human-machine interaction to extract information from data using natural language queries. A large quantity of data is encapsulated in structured formats, e.g., relational databases. Diagnosing Issues and Finding Solutions. Developing NLP for Automated Question Answering. Utilize all transformer based models (BERT & co.) and smoothly … b) Knowledge-based question answering is the idea of answering a natural language question by mapping it to a query over a structured database. The document retriever functions as the search engine, ranking and retrieving relevant documents to which it has access. I recently completed a course on NLP through Deep Learning (CS224N) at Stanford and loved the experience. Evaluating QA: Metrics, Predictions, and the Null Response. Another area where QA systems will shine is in corporate and general use chatbots. The answer type specifies the kind of entity the answer consists of (person, location, time, etc.). One example of such a system is IBM’s Watson, which won on Jeopardy! This is called ‘automated question answering’ and it is the NLP project we are going to implement today. So let's dive in and see how you can do this. A subfield of Question Answering … Question answering seeks to extract information from data and, generally speaking, data come in two broad formats: structured and unstructured. The new algorithms, especially deep learning based algorithms have made a decent progress in text and image classification. Some QA systems exploit a hybrid design that harvests information from both data types; IBM’s Watson is a famous example. Supervised methods generalize this approach and are used when there exists a dataset of question-logical form pairs, such as in the figure above. In spite of being one of the oldest research areas, QA has application in a wide variety of tasks, such as information retrieval and entity extraction. Recently, QA has also been used to develop dialog systems [1] and chatbots [2] designed to simulate human conversation. One need only feed the question and the passage into the model and wait for the answer. analytics. QA systems operate within a domain, constrained by the data that is provided to them. In other recent question-answering NLP news, last week Google AI together with partners from University of Washington and Princeton University … In our earlier example, “when was Employee Name hired?”, the focus would be “when” and the answer type might be a numeric date-time.

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