Henry Zhu

Sep 22, 2023 | amazon.science | Shen Wang |Xiaofei Ma |Henry Zhu |Danilo Neves Ribeiro

We introduce STREET, a unified multi-task and multi-domain natural language reasoning and explanation benchmark. Unlike most existing question-answering (QA) datasets, we expect models to not only answer questions, but also produce step-by-step structured explanations describing how premises in the question are used to produce intermediate conclusions that can prove the correctness of a certain answer.

Jun 9, 2023 | amazon.science | Henry Zhu |Sheng Zhang |Pramuditha Perera

Entities can be expressed in diverse formats, such as texts, images, or column names and cell values in tables. While existing entity linking (EL) models work well on per modality configuration, such as text-only EL, visual grounding, or schema linking, it is more challenging to design a unified model for diverse modality configurations.

Jun 9, 2023 | amazon.science | Sheng Zhang |Gukyeong Kwon |Pramuditha Perera |Henry Zhu

The open-ended Visual Question Answering (VQA) task requires AI models to jointly reason over visual and natural language inputs using world knowledge. Recently, pre-trained Language Models (PLM) such as GPT-3 have been applied to the task and shown to be powerful world knowledge sources.