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Found 15 papers, 7 papers with code
NJU’s submission to the WMT20 QE Shared Task
This paper describes our system of the sentence-level and word-level Quality Estimation Shared Task of WMT20.
HW-TSC’s Participation at WMT 2021 Quality Estimation Shared Task
This paper presents our work in WMT 2021 Quality Estimation (QE) Shared Task.
Alleviating Distribution Shift in Synthetic Data for Machine Translation Quality Estimation
Quality Estimation (QE) models evaluate the quality of machine translations without reference translations, serving as the reward models for the translation task.
"I've Heard of You!": Generate Spoken Named Entity Recognition Data for Unseen Entities
Spoken named entity recognition (NER) aims to identify named entities from speech, playing an important role in speech processing.
Why Not Transform Chat Large Language Models to Non-English?
For the second issue, we propose a method comprising two synergistic components: low-rank adaptation for training to maintain the original LLM parameters, and recovery KD, which utilizes data generated by the chat LLM itself to recover the original knowledge from the frozen parameters.
Improved Paraphrase Generation via Controllable Latent Diffusion
Paraphrase generation strives to generate high-quality and diverse expressions of a given text, a domain where diffusion models excel.
From Handcrafted Features to LLMs: A Brief Survey for Machine Translation Quality Estimation
Machine Translation Quality Estimation (MTQE) is the task of estimating the quality of machine-translated text in real time without the need for reference translations, which is of great importance for the development of MT.
Lost in the Source Language: How Large Language Models Evaluate the Quality of Machine Translation
This study investigates how Large Language Models (LLMs) leverage source and reference data in machine translation evaluation task, aiming to better understand the mechanisms behind their remarkable performance in this task.
MAPO: Advancing Multilingual Reasoning through Multilingual Alignment-as-Preference Optimization
To enhance reasoning abilities in non-dominant languages, we propose a Multilingual-Alignment-as-Preference Optimization framework (MAPO), aiming to align the reasoning processes in other languages with the dominant language.
Unify word-level and span-level tasks: NJUNLP's Participation for the WMT2023 Quality Estimation Shared Task
We generate pseudo MQM data using parallel data from the WMT translation task.
CoP: Factual Inconsistency Detection by Controlling the Preference
To separate the preference for factual consistency, we propose an unsupervised framework named CoP by controlling the preference of the generation model with the help of prompt.
DirectQE: Direct Pretraining for Machine Translation Quality Estimation
However, we argue that there are gaps between the predictor and the estimator in both data quality and training objectives, which preclude QE models from benefiting from a large number of parallel corpora more directly.
Optimizing Large-Scale Hyperparameters via Automated Learning Algorithm
Modern machine learning algorithms usually involve tuning multiple (from one to thousands) hyperparameters which play a pivotal role in terms of model generalizability.
Quadruply Stochastic Gradients for Large Scale Nonlinear Semi-Supervised AUC Optimization
To address this problem, in this paper, we propose a novel scalable quadruply stochastic gradient algorithm (QSG-S2AUC) for nonlinear semi-supervised AUC optimization.
Scalable Semi-Supervised SVM via Triply Stochastic Gradients
Specifically, to handle two types of data instances involved in S$^3$VM, TSGS$^3$VM samples a labeled instance and an unlabeled instance as well with the random features in each iteration to compute a triply stochastic gradient.
