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Tagyoung Chung

As seen in: Amazon Science
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Articles

Fine-to-coarse entailment hierarchy construction for coarse-to-fine story generation

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By Nanyun Peng, MOHIT BANSAL, Tagyoung Chung, Haw-Shiuan Chang
| Amazon Science
When users want to write a story with a language model (LM) assistant such as ChatGPT, it is often very difficult to provide a prompt that clearly specifies all their interests. For the providers of LM assistants, it is also difficult to ensure their output stories come from a dataset without copyright concerns.
Open in Who Shared Issue with article?

Simple Question Answering with Subgraph Ranking and Joint-Scoring

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By Wenbo Zhao, Tagyoung Chung, Anuj Goyal, Angeliki Metallinou
| Amazon Science
Amazon Fulfillment Planning & Execution (FPX) Science team within Supply Chain Optimization Technologies (SCOT) Fulfilment Optimization group is seeking a Principal Research Scientist with expertise in Machine Learning and a proven record of solving business problems through scalable ML solutions. Network Planning and Fulfillment Execution tackles some of the most mathematically complex challenges in facility and transportation planning to improve Amazon's operational efficiency worldwide.
Open in Who Shared View Duplicates (7) Issue with article?

MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension

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By Di Jin, Shuyang Gao, Jiun-Yu Kao, Tagyoung Chung
| Amazon Science
Machine Reading Comprehension (MRC) for question answering (QA), which aims to answer a question given the relevant context passages, is an important way to test the ability of intelligence systems to understand human language.
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