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1. THE BAR IN BOUNDLESS GAME SHOWING HOME COLR CODES MOVIE
2. THE BAR IN BOUNDLESS GAME SHOWING HOME COLR CODES FREE

Job summaryAmazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. Q: Which member of the Red Hot Chili Peppers appeared in Point Break? Roosevelt was first elected, how long had it been since someone in his party won the presidential election?

THE BAR IN BOUNDLESS GAME SHOWING HOME COLR CODES MOVIE

Q: Which Studio Ghibli movie scored the lowest on Rotten Tomatoes?

For example, given the question “How many Oscars did Argo win?”, a worker could identify the film Argo as an entity and link to its Wikidata URL.Įxamples of Mintaka questions are shown below: Next, we created an entity-linking task where workers were shown question-answer pairs from the previous task and asked to either identify or verify the entities in either the question or answer and provide supporting evidence from Wikipedia entries.

THE BAR IN BOUNDLESS GAME SHOWING HOME COLR CODES FREE

They were collected as free text, with no restrictions on what sources could be used. Question-answer pairs were limited to eight categories: movies, music, sports, books, geography, politics, video games, and history. New metric can be calculated 55 times as quickly as its state-of-the-art predecessor, making it practical for model training. We also ground Mintaka in the Wikidata knowledge graph by linking entities in the question text and answer text to Wikidata IDs. Mintaka is a large, complex, natural, and multilingual question-answer dataset with 20,000 questions collected in English and professionally translated into eight languages: Arabic, French, German, Hindi, Italian, Japanese, Portuguese, and Spanish. To help bridge this gap, we have publicly released a new dataset: Mintaka, which we describe in a paper we're presenting at this year’s International Conference on Computational Linguistics (COLING). A majority of QA datasets are also only in English. Most existing QA datasets are large but simple, complex but small, or large and complex but synthetically generated, so they are less natural. While many state-of-the art question-answering models get good performance on simple questions, complex questions remain an open problem. For example, the question “What movie had a higher budget, Titanic or Men in Black II?” is a complex question because it requires looking up two different facts ( Titanic | budget | 200 million USD and Men in Black II | budget | 140 million USD), followed by a calculation to compare values ( 200 million USD > 140 million USD). Novel pretraining method enables increases of 5% to 14% on five different evaluation metrics.