Graham Neubig
Biographic Data
| ID | 5659675 |
|---|---|
| NAME | Graham Neubig |
| GIVEN NAMES | Graham |
| FAMILY NAME | Neubig |
| SIGNATURE | NEUBIG G |
| AFFILIATIONS | Graduate School of Information Science Nara Institute of Science and Technology |
| ORCID | 0000-0002-2072-3789 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2016 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 0 |
Pre-train, Prompt, and Predict
This article surveys and organizes research works in a new paradigm in natural language processing, which we dub “prompt-based learning.” Unlike traditional supervised learning, which trains a model to take in an input x and predict an output y as P ( y|x ), prompt-based learning is based on language models that model the probability of text directly. To use these models to perform prediction tasks, the original input x is modified using a templa…
Optimization for Statistical Machine Translation
In statistical machine translation (SMT), the optimization of the system parameters to maximize translation accuracy is now a fundamental part of virtually all modern systems. In this article, we survey 12 years of research on optimization for SMT, from the seminal work on discriminative models (Och and Ney 2002) and minimum error rate training (Och 2003), to the most recent advances. Starting with a brief introduction to the fundamentals of SMT …
No prominent works on this page.
Optimization for Statistical Machine Translation
In statistical machine translation (SMT), the optimization of the system parameters to maximize translation accuracy is now a fundamental part of virtually all modern systems. In this article, we survey 12 years of research on optimization for SMT, from the seminal work on discriminative models (Och and Ney 2002) and minimum error rate training (Och 2003), to the most recent advances. Starting with a brief introduction to the fundamentals of SMT …
Pre-train, Prompt, and Predict
This article surveys and organizes research works in a new paradigm in natural language processing, which we dub “prompt-based learning.” Unlike traditional supervised learning, which trains a model to take in an input x and predict an output y as P ( y|x ), prompt-based learning is based on language models that model the probability of text directly. To use these models to perform prediction tasks, the original input x is modified using a templa…
Computer Science (2 works) · Natural Language Processing Techniques (2 works) · Topic Modeling (2 works) · Algorithm (1 work) · Algorithms and Data Compression (1 work) · Artificial Intelligence (1 work) · Artificial Intelligence (1 work) · Cover (algebra) (1 work) · Discriminative model (1 work) · Field (mathematics) (1 work)