Search Results for author: V. G. Vinod Vydiswaran

Found 3 papers, 1 papers with code

IDPG: An Instance-Dependent Prompt Generation Method

no code implementations NAACL 2022 Zhuofeng Wu, Sinong Wang, Jiatao Gu, Rui Hou, Yuxiao Dong, V. G. Vinod Vydiswaran, Hao Ma

Prompt tuning is a new, efficient NLP transfer learning paradigm that adds a task-specific prompt in each input instance during the model training stage.

Language Modelling Natural Language Understanding +1

PharmMT: A Neural Machine Translation Approach to Simplify Prescription Directions

no code implementations Findings of the Association for Computational Linguistics 2020 Jiazhao Li, Corey Lester, Xinyan Zhao, Yuting Ding, Yun Jiang, V. G. Vinod Vydiswaran

We propose a novel machine translation-based approach, PharmMT, to automatically and reliably simplify prescription directions into patient-friendly language, thereby significantly reducing pharmacist workload.

Machine Translation Translation

LIREx: Augmenting Language Inference with Relevant Explanation

1 code implementation16 Dec 2020 Xinyan Zhao, V. G. Vinod Vydiswaran

Natural language explanations (NLEs) are a special form of data annotation in which annotators identify rationales (most significant text tokens) when assigning labels to data instances, and write out explanations for the labels in natural language based on the rationales.

Explanation Generation Natural Language Inference

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