Few-shot learning is a technique in which we attempt to learn a general machine learning model for a set of related tasks and then customize it to new tasks ...

As natural-language processing (NLP) has become more integral to our daily lives, the ability to accurately evaluate NLP models has grown in importance. ...

Abstractive summarization is the automatic extraction and recombination of phrases from a text in order to summarize that text. Deep-learning-based ...

Natural-language understanding and question answering are areas of focus, with additional topics ranging from self-learning to text summarization. Source ...

Question answering is a popular task in natural-language processing, where models are given questions such as “What city is the Mona Lisa in?” and trained ...

Question-answering (QA) models sometimes need to retrieve information from tables, which use an entirely different set of semantic cues than free-form text. ...

Machines hoping to converse convincingly with humans have several natural-language-processing (NLP) skills to master, including text summarization, ...

Amazon’s 45-plus papers at the annual meeting of the North American chapter of the Association for Computational Linguistics, which begins next week, sorted ...

Question answering (QA) is the machine learning task of learning to predict answers to questions. For example, given the question, “Where was Natalie ...

Between the main conference and the recently inaugurated ACL Proceedings, Amazon researchers have more than 65 papers at this year's meeting of the ...