EMNLP
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A quick guide to Amazon’s 50-plus papers at EMNLP 2024
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Large language models (LLMs) have come to dominate the field of natural-language processing, so it’s no surprise that they also dominate the research that ...

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The FEVER data set: What doesn’t kill it will make it stronger
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This year at the Conference on Empirical Methods in Natural-Language Processing (EMNLP), we will cohost the Second Workshop on Fact Extraction and ...

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Amazon panel discusses latest in NLP and conversational AI
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Amazon Devices Sales and Customer Experience is looking for a talented Applied Scientist to help ...

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As voice agents proliferate, EMNLP broadens its scope
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At this year’s Conference on Empirical Methods in Natural Language Processing (EMNLP), Julia Hirschberg, an Amazon Scholar and a professor of computer ...

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How to make AI better at reading comprehension
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Question answering through reading comprehension is a popular task in natural-language processing. It’s a task many people know from standardized tests: a ...

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Mitigating social bias in knowledge graph embeddings
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Question-answering systems frequently rely on knowledge graphs, large collections of facts about real-world entities (people, organizations, countries, ...

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Alexa & Friends features Pradeep Natarajan, Alexa AI principal applied scientist
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Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply ...

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EMNLP: Mitigating bias and “getting closer to the user”
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In recent years, mitigating bias in machine learning models has become a major topic of research, and that’s as true in natural-language processing as in ...

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Amazon’s 23 papers at EMNLP 2021
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Natural-language understanding and question answering are areas of focus, with additional topics ranging from self-learning to text summarization. Source ...

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Adapting machine translation models to new genres
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Neural machine translation systems are often optimized to perform well for specific text genres or domains, such as newspaper articles, user manuals, or ...

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