2 citations · 3 across the 12 of their papers we have counts for
12 papers
Last Translation Benchmark
Vilém Zouhar, Niyati Bafna, Mukund Choudhary +263
For scientific progress, we need benchmarks that test the limits of state-of-the-art models, and evaluation methods that inform us about failure cases. As models get stronger, stan…
What Does LLM Refinement Actually Improve? A Systematic Study on Document-Level Literary Translation
Shaomu Tan, Dawei Zhu, Ke Tran +5
Iterative self-refinement is a simple inference-time strategy for machine translation: an LLM revises its own translation over multiple inference-time passes. Yet document-scale re…
Remedy-R: Generative Reasoning for Machine Translation Evaluation without Error Annotations
Shaomu Tan, Ryosuke Mitani, Ritvik Choudhary +3
Over the years, automatic MT metrics have hillclimbed benchmarks and presented strong and sometimes human-level agreement with human ratings. Yet they remain black-box, offering li…
Investigating Test-Time Scaling with Reranking for Machine Translation
Shaomu Tan, Ryosuke Mitani, Ritvik Choudhary +1
Scaling model parameters has become the de facto strategy for improving NLP systems, but it comes with substantial computational costs. Test-Time Scaling (TTS) offers an alternativ…
Remedy: Learning Machine Translation Evaluation from Human Preferences with Reward Modeling
Shaomu Tan, Christof Monz
A key challenge in MT evaluation is the inherent noise and inconsistency of human ratings. Regression-based neural metrics struggle with this noise, while prompting LLMs shows prom…
Neuron Specialization: Leveraging intrinsic task modularity for multilingual machine translation
Shaomu Tan, Di Wu, Christof Monz
Training a unified multilingual model promotes knowledge transfer but inevitably introduces negative interference. Language-specific modeling methods show promise in reducing inter…
How Far Can 100 Samples Go? Unlocking Overall Zero-Shot Multilingual Translation via Tiny Multi-Parallel Data
Di Wu, Shaomu Tan, Yan Meng +2
Zero-shot translation aims to translate between language pairs not seen during training in Multilingual Machine Translation (MMT) and is largely considered an open problem. A commo…
Towards a Better Understanding of Variations in Zero-Shot Neural Machine Translation Performance
Shaomu Tan, Christof Monz
Multilingual Neural Machine Translation (MNMT) facilitates knowledge sharing but often suffers from poor zero-shot (ZS) translation qualities. While prior work has explored the cau…
UvA-MT's Participation in the WMT23 General Translation Shared Task
Di Wu, Shaomu Tan, David Stap +2
This paper describes the UvA-MT's submission to the WMT 2023 shared task on general machine translation. We participate in the constrained track in two directions: English <-> Hebr…
Document AI: A Comparative Study of Transformer-Based, Graph-Based Models, and Convolutional Neural Networks For Document Layout Analysis
Sotirios Kastanas, Shaomu Tan, Yi He
Document AI aims to automatically analyze documents by leveraging natural language processing and computer vision techniques. One of the major tasks of Document AI is document layo…
Make Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-Tuning
Baohao Liao, Shaomu Tan, Christof Monz
Parameter-efficient fine-tuning (PEFT) of pre-trained language models (PLMs) has emerged as a highly successful approach, with training only a small number of parameters without sa…
Towards leveraging latent knowledge and Dialogue context for real-world conversational question answering
Shaomu Tan, Denis Paperno
In many real-world scenarios, the absence of external knowledge source like Wikipedia restricts question answering systems to rely on latent internal knowledge in limited dialogue…