This work proposes augmenting existing benchmarks to increase translation difficulty by combining adversarial optimization with a differentiable translation difficulty estimator, and uses gradients from a combined difficulty and fluency objective to iteratively replace tokens in Adversarial Translation Optimization (ATO).
Abstract
As state-of-the-art machine translation models saturate standard benchmarks, the field needs more challenging evaluations to distinguish between models of varying quality. We propose augmenting existing benchmarks to increase translation difficulty by combining adversarial optimization with a differentiable translation difficulty estimator. Our Adversarial Translation Optimization (ATO) uses gradients from a combined difficulty and fluency objective to iteratively replace tokens. Because each step branches over candidate substitutions at every position, optimization becomes a tree search problem, which we address with Beam Search. ATO offers a gradient-based alternative to LLM-based dataset creation without LLM prompting, expensive human curation, or task-specific model training. Our ATO-modified benchmark lowers average translation quality (xCOMET) from 0.93 to 0.82, compared to 0.88 for paraphrasing and 0.86 for a zero-shot baseline. Human evaluation shows the modified texts are somewhat less natural than the baselines but remain reasonably grammatical and plausible while being substantially harder to translate. We release two datasets of 350 English texts each, generated by our methods, as well as the code.
Large language models continue to face challenges in translating low-resource languages with scarce parallel data. This study investigates how to fine-tune them effectively using target-side monolingual data. Existing approaches—dominated by back-translation and recent LLM-based rewriting—remain limited by noisy synthetic sources, unguided simplification, and the absence of a principled mechanism for integrating monolingual sentences into the training objective. To address this, we developed a semi-supervised framework that integrates marginal distribution estimation and curriculum-guided rewriting to exploit monolingual data for low-resource translation. Experiments in four low-resource directions demonstrated substantial gains, averaging +8 spBLEU and +10 COMET over strong baselines, while three additional mid-resource directions showed stable improvements and consistent trends. Reference-free metrics further validated robust gains in fluency and adequacy. The findings establish a scalable paradigm for low-resource translation, revealing that the principled integration of marginal likelihood estimation and generative rewriting enables large language models to achieve superior performance under extreme data scarcity.
Wenjie Yu, Zhiqiang Yu, Zuo Jiang et al.· ACM Transactions on Asian an...· 0 citations
An Efficient Dual-BERT Adversarial Network (DBAN) is proposed to improve the translation of noisy UGC by integrating contextual representation learning with adversarial training and significantly improves contextual understanding and cross-lingual semantic alignment while maintaining computational efficiency.
A. A. Aliero, Nasiru Muhammad Dankolo· International Journal Of Eng...· 0 citations
This work studies reference-free post-training for multilingual machine translation with open large language models and finds that on-policy distillation reaches, but does not surpass, the quality frontier achieved by RL with checkpoint interpolation.
Chris Han, Pengzhi Gao, Pei Fu et al.· 0 citations
This work proposes a distillation approach based on ranking supervision that consistently outperforms supervised fine-tuning as well as FKL and RKL baselines in Python code generation, multilingual generation, and data-science scenarios and offers guidance for future research in model compression.
Zhe Ding, Hui Ji, Su Pan et al.· Neural Networks· 0 citations
The research offers a Lotus Effect-Attention-based Bi-directional Gated Recurrent Unit (LE-Att-Bi-GRU) deep learning model for automatic translation quality assessment that improves semantic representation by incorporating a lotus-inspired division method that decreases noise and focuses essential semantic cues.
Neural machine translation (NMT) has witnessed substantial advancements, leveraging its learning capability to deliver highly accurate translations. Nevertheless, the efficacy of NMT models is contingent upon the accessibility of extensive-scale, high-quality training data, and its performance suffers notably in the absence of such datasets. To tackle this challenge, we propose a semantic distance augmentation (SDA) method that integrates syntactic information from constituency parse trees into the NMT encoder to optimize self-attention. Specifically, the source language sentences in the training set are analyzed by constituency parse analysis and the semantic distance attention matrix is constructed. Then, a fusion strategy is designed to integrate this matrix into the self-attention weight, enhancing the representation of the source sentences. In addition, a SDA length-aware strategy is proposed to adaptively control the contribution of semantic distance in the attention computation. Empirical evaluations across multiple low-resource language pairs reveal that the SDA method achieves statistically significant improvements in translation quality over the strong baseline, without requiring additional training data or increasing model complexity.
Fuxue Li, Hong Yan, Chuncheng Chi et al.· PeerJ Computer Science· 0 citations