297 citations · 716 across the 31 of their papers we have counts for
32 papers
Scaling Sign Language Translation
Biao Zhang, Garrett Tanzer, Orhan Firat
Sign language translation (SLT) addresses the problem of translating information from a sign language in video to a spoken language in text. Existing studies, while showing progres…
YouTube-SL-25: A Large-Scale, Open-Domain Multilingual Sign Language Parallel Corpus
Garrett Tanzer, Biao Zhang
Even for better-studied sign languages like American Sign Language (ASL), data is the bottleneck for machine learning research. The situation is worse yet for the many other sign l…
Many-Shot In-Context Learning
Rishabh Agarwal, Avi Singh, Lei M. Zhang +12
Large language models (LLMs) excel at few-shot in-context learning (ICL) -- learning from a few examples provided in context at inference, without any weight updates. Newly expande…
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Gemini Team, Petko Georgiev, Ving Ian Lei +1132
In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over…
Direct Language Model Alignment from Online AI Feedback
Shangmin Guo, Biao Zhang, Tianlin Liu +9
Direct alignment from preferences (DAP) methods, such as DPO, have recently emerged as efficient alternatives to reinforcement learning from human feedback (RLHF), that do not requ…
When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method
Biao Zhang, Zhongtao Liu, Colin Cherry +1
While large language models (LLMs) often adopt finetuning to unlock their capabilities for downstream applications, our understanding on the inductive biases (especially the scalin…
MADLAD-400: A Multilingual And Document-Level Large Audited Dataset
Sneha Kudugunta, Isaac Caswell, Biao Zhang +8
We introduce MADLAD-400, a manually audited, general domain 3T token monolingual dataset based on CommonCrawl, spanning 419 languages. We discuss the limitations revealed by self-a…
SLTUNET: A Simple Unified Model for Sign Language Translation
Biao Zhang, Mathias Müller, Rico Sennrich
Despite recent successes with neural models for sign language translation (SLT), translation quality still lags behind spoken languages because of the data scarcity and modality ga…
When Does Monolingual Data Help Multilingual Translation: The Role of Domain and Model Scale
Christos Baziotis, Biao Zhang, Alexandra Birch +1
Multilingual machine translation (MMT), trained on a mixture of parallel and monolingual data, is key for improving translation in low-resource language pairs. However, the literat…
Efficient CTC Regularization via Coarse Labels for End-to-End Speech Translation
Biao Zhang, Barry Haddow, Rico Sennrich
For end-to-end speech translation, regularizing the encoder with the Connectionist Temporal Classification (CTC) objective using the source transcript or target translation as labe…
Prompting Large Language Model for Machine Translation: A Case Study
Biao Zhang, Barry Haddow, Alexandra Birch
Research on prompting has shown excellent performance with little or even no supervised training across many tasks. However, prompting for machine translation is still under-explor…
Revisiting End-to-End Speech-to-Text Translation From Scratch
Biao Zhang, Barry Haddow, Rico Sennrich
End-to-end (E2E) speech-to-text translation (ST) often depends on pretraining its encoder and/or decoder using source transcripts via speech recognition or text translation tasks,…
Examining Scaling and Transfer of Language Model Architectures for Machine Translation
Biao Zhang, Behrooz Ghorbani, Ankur Bapna +4
Natural language understanding and generation models follow one of the two dominant architectural paradigms: language models (LMs) that process concatenated sequences in a single s…
Data Scaling Laws in NMT: The Effect of Noise and Architecture
Yamini Bansal, Behrooz Ghorbani, Ankush Garg +5
In this work, we study the effect of varying the architecture and training data quality on the data scaling properties of Neural Machine Translation (NMT). First, we establish that…
Sparse Attention with Linear Units
Biao Zhang, Ivan Titov, Rico Sennrich
Recently, it has been argued that encoder-decoder models can be made more interpretable by replacing the softmax function in the attention with its sparse variants. In this work, w…
Fast Interleaved Bidirectional Sequence Generation
Biao Zhang, Ivan Titov, Rico Sennrich
Independence assumptions during sequence generation can speed up inference, but parallel generation of highly inter-dependent tokens comes at a cost in quality. Instead of assuming…
Adaptive Feature Selection for End-to-End Speech Translation
Biao Zhang, Ivan Titov, Barry Haddow +1
Information in speech signals is not evenly distributed, making it an additional challenge for end-to-end (E2E) speech translation (ST) to learn to focus on informative features. I…
On Sparsifying Encoder Outputs in Sequence-to-Sequence Models
Biao Zhang, Ivan Titov, Rico Sennrich
Sequence-to-sequence models usually transfer all encoder outputs to the decoder for generation. In this work, by contrast, we hypothesize that these encoder outputs can be compress…
A Study in Improving BLEU Reference Coverage with Diverse Automatic Paraphrasing
Rachel Bawden, Biao Zhang, Lisa Yankovskaya +2
We investigate a long-perceived shortcoming in the typical use of BLEU: its reliance on a single reference. Using modern neural paraphrasing techniques, we study whether automatica…
Root Mean Square Layer Normalization
Biao Zhang, Rico Sennrich
Layer normalization (LayerNorm) has been successfully applied to various deep neural networks to help stabilize training and boost model convergence because of its capability in ha…
Improving Deep Transformer with Depth-Scaled Initialization and Merged Attention
Biao Zhang, Ivan Titov, Rico Sennrich
The general trend in NLP is towards increasing model capacity and performance via deeper neural networks. However, simply stacking more layers of the popular Transformer architectu…
A Lightweight Recurrent Network for Sequence Modeling
Biao Zhang, Rico Sennrich
Recurrent networks have achieved great success on various sequential tasks with the assistance of complex recurrent units, but suffer from severe computational inefficiency due to…
Simplifying Neural Machine Translation with Addition-Subtraction Twin-Gated Recurrent Networks
Biao Zhang, Deyi Xiong, Jinsong Su +2
In this paper, we propose an additionsubtraction twin-gated recurrent network (ATR) to simplify neural machine translation. The recurrent units of ATR are heavily simplified to hav…
Otem&Utem: Over- and Under-Translation Evaluation Metric for NMT
Jing Yang, Biao Zhang, Yue Qin +3
Although neural machine translation(NMT) yields promising translation performance, it unfortunately suffers from over- and under-translation is- sues [Tu et al., 2016], of which st…