1.3k citations · 2.8k across the 25 of their papers we have counts for
28 papers
Synthetic Data Generation and Joint Learning for Robust Code-Mixed Translation
Kartik Kartik, Sanjana Soni, Anoop Kunchukuttan +2
The widespread online communication in a modern multilingual world has provided opportunities to blend more than one language (aka code-mixed language) in a single utterance. This…
UniMax: Fairer and more Effective Language Sampling for Large-Scale Multilingual Pretraining
Hyung Won Chung, Noah Constant, Xavier Garcia +4
Pretrained multilingual large language models have typically used heuristic temperature-based sampling to balance between different languages. However previous work has not systema…
Character-Aware Models Improve Visual Text Rendering
Rosanne Liu, Dan Garrette, Chitwan Saharia +7
Current image generation models struggle to reliably produce well-formed visual text. In this paper, we investigate a key contributing factor: popular text-to-image models lack cha…
FRMT: A Benchmark for Few-Shot Region-Aware Machine Translation
Parker Riley, Timothy Dozat, Jan A. Botha +5
We present FRMT, a new dataset and evaluation benchmark for Few-shot Region-aware Machine Translation, a type of style-targeted translation. The dataset consists of professional tr…
Bidirectional Language Models Are Also Few-shot Learners
Ajay Patel, Bryan Li, Mohammad Sadegh Rasooli +3
Large language models such as GPT-3 (Brown et al., 2020) can perform arbitrary tasks without undergoing fine-tuning after being prompted with only a few labeled examples. An arbitr…
Reducing Retraining by Recycling Parameter-Efficient Prompts
Brian Lester, Joshua Yurtsever, Siamak Shakeri +1
Parameter-efficient methods are able to use a single frozen pre-trained large language model (LLM) to perform many tasks by learning task-specific soft prompts that modulate model…