Series against the Dodgers, pitcher Don Larsen threw the only perfect game in On October 8, 1956, in Game Five of the 1956 World Series, after five previous Series losses to them, but the Yankees came back In, the Dodgers finally beat the Yankees in the World In 1954, the Yankees won over 100 games, but the Indians took the pennant withĪn AL record 111 wins 1954 was famously referred to as "The Year the Yankees > process('Who was the winning pitcher in the 1956 World Series?') "42nd Life." It is devoted to this concept in the book series, and severalĪttempts at recreating Milliways, the Restaurant at the End of the Universe, were made. In the online community Second Life, there is a section on a sim calledĤ3. "the answer to the ultimate question of life, the universe and everything" asĤ2. Similarly, DuckDuckGo also gives the result of "the answer to life the universe and everything" as 42, as will Wolfram'sĬomputational Knowledge Engine. Google Calculator will give the result to Several online calculators areĪlso programmed with the Question.
Many chatbots, when askedĪbout the meaning of life, will answer "42". Invoked in similar ways to mean "anything at all". "Life, the universe, and everything" isĪ common name for the off-topic section of an Internet forum and the phrase is The number 42 and the phrase, "Life, the universe, and everything" haveĪttained cult status on the Internet. | 1 | 42 | Phrases from The Hitchhiker's Guide to the Galaxy | 47242 | 141.26 | > process('What is the answer to life, the universe, and everything?') Information retrieval and natural language processing (NLP), which isĬoncerned with building systems that automatically answer questions posed by Question Answering (QA) is a computer science discipline within the fields of | 1 | a computer science discipline within the fields of information retrieval and natural language processing | Question answering | 1917.8 | 327.89 | | Rank | Answer | Doc | Answer Score | Doc Score | For each question, the top span and the Wikipedia paragraph it came from are returned. Run python scripts/pipeline/interactive.py to drop into an interactive session.
Install DrQA and download our models to start asking open-domain questions! Reproduction numbers are very similar but not exact. Note that this work is a refactored and more efficient version of the original code. We also list several different datasets for evaluation, see QA Datasets. This repository includes code, data, and pre-trained models for processing and querying Wikipedia as described in the paper - see Trained Models and Data. As a result, DrQA can be straightforwardly applied to any collection of documents, as described in the retriever README. Note that DrQA treats Wikipedia as a generic collection of articles and does not rely on its internal graph structure. In order to answer any question, one must first retrieve the few potentially relevant articles among more than 5 million, and then scan them carefully to identify the answer. Wikipedia is a well-suited source of large-scale, rich, detailed information. Our experiments with DrQA focus on answering factoid questions while using Wikipedia as the unique knowledge source for documents. Thus the system has to combine the challenges of document retrieval (finding the relevant documents) with that of machine comprehension of text (identifying the answers from those documents).
In this setting, we are searching for an answer to a question in a potentially very large corpus of unstructured documents (that may not be redundant). In particular, DrQA is targeted at the task of "machine reading at scale" (MRS). Quick LinksĭrQA is a system for reading comprehension applied to open-domain question answering. This is a PyTorch implementation of the DrQA system described in the ACL 2017 paper Reading Wikipedia to Answer Open-Domain Questions.