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We want to maintain that quality even as billions of people start using Quora.We have a huge volume of textual data via millions of questions, answers, and comments on Quora.Deep learning has recently begun to have an important impact on this kind of task.Document summarization is the task where a short description of a text document is created.A language model is used to create the caption that is conditioned on the image.Some examples include: This is not just an application for the hearing impaired, but also in generating human readable text for image and video data that can be searched, such as on the web.The field of natural language processing is shifting from statistical methods to neural network methods.There are still many challenging problems to solve in natural language.

Given an utterance of text as audio data, the model must produce human readable text.A popular classification example is sentiment analysis where class labels represent the emotional tone of the source text such as “ Further, the problem may be framed in a way that requires multiple classes assigned to a text, so-called multi-label classification.Such as predicting multiple hashtags for a source tweet. the problem is to predict the next word given the previous words.We also have an enormous amount of metadata to complement our text corpus, including user upvotes & downvotes, user-topic interest & expertise relationships, question-topic relationships, the topic ontology, a social and influence graph of users, and a history of actions taken by users on Quora.Such a rich dataset puts us in a unique position to use various Natural Language Processing (NLP) techniques to solve exciting problems critical to our success.

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Most references are drawn from Goldberg’s excellent 2015 primer on deep learning for NLP researchers.

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