566 citations · 688 across the 11 of their papers we have counts for
13 papers
Do GPTs Produce Less Literal Translations?
Vikas Raunak, Arul Menezes, Matt Post +1
Large Language Models (LLMs) such as GPT-3 have emerged as general-purpose language models capable of addressing many natural language generation or understanding tasks. On the tas…
Deliberate then Generate: Enhanced Prompting Framework for Text Generation
Bei Li, Rui Wang, Junliang Guo +7
Large language models (LLMs) have shown remarkable success across a wide range of natural language generation tasks, where proper prompt designs make great impacts. While existing…
Leveraging GPT-4 for Automatic Translation Post-Editing
Vikas Raunak, Amr Sharaf, Yiren Wang +2
While Neural Machine Translation (NMT) represents the leading approach to Machine Translation (MT), the outputs of NMT models still require translation post-editing to rectify erro…
ResiDual: Transformer with Dual Residual Connections
Shufang Xie, Huishuai Zhang, Junliang Guo +6
Transformer networks have become the preferred architecture for many tasks due to their state-of-the-art performance. However, the optimal way to implement residual connections in…
Rank-One Editing of Encoder-Decoder Models
Vikas Raunak, Arul Menezes
Large sequence to sequence models for tasks such as Neural Machine Translation (NMT) are usually trained over hundreds of millions of samples. However, training is just the origin…
Operationalizing Specifications, In Addition to Test Sets for Evaluating Constrained Generative Models
Vikas Raunak, Matt Post, Arul Menezes
In this work, we present some recommendations on the evaluation of state-of-the-art generative models for constrained generation tasks. The progress on generative models has been r…
VideoDubber: Machine Translation with Speech-Aware Length Control for Video Dubbing
Yihan Wu, Junliang Guo, Xu Tan +7
Video dubbing aims to translate the original speech in a film or television program into the speech in a target language, which can be achieved with a cascaded system consisting of…
Finding Memo: Extractive Memorization in Constrained Sequence Generation Tasks
Vikas Raunak, Arul Menezes
Memorization presents a challenge for several constrained Natural Language Generation (NLG) tasks such as Neural Machine Translation (NMT), wherein the proclivity of neural models…
SALTED: A Framework for SAlient Long-Tail Translation Error Detection
Vikas Raunak, Matt Post, Arul Menezes
Traditional machine translation (MT) metrics provide an average measure of translation quality that is insensitive to the long tail of behavioral problems in MT. Examples include t…
To Ship or Not to Ship: An Extensive Evaluation of Automatic Metrics for Machine Translation
Tom Kocmi, Christian Federmann, Roman Grundkiewicz +3
Automatic metrics are commonly used as the exclusive tool for declaring the superiority of one machine translation system's quality over another. The community choice of automatic…
The Curious Case of Hallucinations in Neural Machine Translation
Vikas Raunak, Arul Menezes, Marcin Junczys-Dowmunt
In this work, we study hallucinations in Neural Machine Translation (NMT), which lie at an extreme end on the spectrum of NMT pathologies. Firstly, we connect the phenomenon of hal…
XLM-T: Scaling up Multilingual Machine Translation with Pretrained Cross-lingual Transformer Encoders
Shuming Ma, Jian Yang, Haoyang Huang +10
Multilingual machine translation enables a single model to translate between different languages. Most existing multilingual machine translation systems adopt a randomly initialize…
Achieving Human Parity on Automatic Chinese to English News Translation
Hany Hassan, Anthony Aue, Chang Chen +21
Machine translation has made rapid advances in recent years. Millions of people are using it today in online translation systems and mobile applications in order to communicate acr…