paper-with-me

홈 › Papers

deepQuest-py: Large and Distilled Models for Quality Estimation

2021-11-01 · EMNLP (ACL) 2021 11 · Fernando Alva-Manchego, Abiola Obamuyide, Amit Gajbhiye, Frédéric Blain, Marina Fomicheva, Lucia Specia

We introduce deepQuest-py, a framework for training and evaluation of large and light-weight models for Quality Estimation (QE). deepQuest-py provides access to (1) state-of-the-art models based on pre-trained Transformers for sentence-level and word-level QE; (2) light-weight and efficient sentence-level models implemented via knowledge distillation; and (3) a web interface for testing models and visualising their predictions. deepQuest-py is available at https://github.com/sheffieldnlp/deepQuest-py under a CC BY-NC-SA licence.

📄 PDF Abstract BibTeX

Code (1)

sheffieldnlp/deepQuest-py 공식 구현

Tasks

Knowledge DistillationSentence

Similar Papers 제목 키워드 기반

DEEPQUESTION: Systematic Generation of Real-World Challenges for Evaluating LLMs Performance

2025-05-30 · Ali Khoramfar, Ali Ramezani, Mohammad Mahdi Mohajeri, Mohammad Javad Dousti 외

LLMs often excel on standard benchmarks but falter on real-world tasks. We introduce DeepQuestion, a scalable automated framework that augments existing datasets based on Bloom's taxonomy and creates novel questions that…

deepQuest: A Framework for Neural-based Quality Estimation

2018-08-01 · COLING 2018 8 · Julia Ive, Fr{\'e}d{\'e}ric Blain, Lucia Specia

Predicting Machine Translation (MT) quality can help in many practical tasks such as MT post-editing. The performance of Quality Estimation (QE) methods has drastically improved recently with the introduction of neural a…

Feature EngineeringMachine TranslationSentenceTranslation

Knowledge Distillation for Quality Estimation

2021-07-01 · Findings (ACL) 2021 8 · Amit Gajbhiye, Marina Fomicheva, Fernando Alva-Manchego, Frédéric Blain 외

Quality Estimation (QE) is the task of automatically predicting Machine Translation quality in the absence of reference translations, making it applicable in real-time settings, such as translating online social media co…

Data AugmentationKnowledge DistillationMachine TranslationTranslation

Knowledge distillation for fast and accurate DNA sequence correction

2022-11-17 · Anastasiya Belyaeva, Joel Shor, Daniel E. Cook, Kishwar Shafin 외

Accurate genome sequencing can improve our understanding of biology and the genetic basis of disease. The standard approach for generating DNA sequences from PacBio instruments relies on HMM-based models. Here, we introd…

Knowledge Distillation

Enhancing AI Face Realism: Cost-Efficient Quality Improvement in Distilled Diffusion Models with a Fully Synthetic Dataset

2025-05-04 · Jakub Wasala, Bartlomiej Wrzalski, Kornelia Noculak, Yuliia Tarasenko 외

This study presents a novel approach to enhance the cost-to-quality ratio of image generation with diffusion models. We hypothesize that differences between distilled (e.g. FLUX.1-schnell) and baseline (e.g. FLUX.1-dev) …

Image GenerationImage-to-Image Translation