| Challenge: | State-of-the-art Quality Estimation models lack robustness to out-of domain examples. |
| Approach: | They propose a method that uses multitask training, data augmentation and contrastive learning to achieve better and more robust QE performance. |
| Outcome: | The proposed method improves QE performance significantly in the MLQE challenge and the robustness of QE models when tested in the Parallel Corpus Mining setup. |
Similar Papers
Improving Translation Quality Estimation with Bias Mitigation (2023.acl-long)
Copied to clipboard
| Challenge: | State-of-the-art translation Quality Estimation models are biased, relying on monolingual features while ignoring the bilingual semantic alignment. |
| Approach: | They propose a method to mitigate the bias of translation quality estimation models by contrastive learning between clean and noisy sentence pairs. |
| Outcome: | The proposed method improves the estimation performance while mitigating the bias. |
Are we Estimating or Guesstimating Translation Quality? (2020.acl-main)
Copied to clipboard
| Challenge: | A carefully engineered ensemble of pre-trained multilingual language models won the QE shared task at WMT19. |
| Approach: | They propose to use pre-trained multilingual language models to train quality estimation for machine translation. |
| Outcome: | A carefully engineered ensemble of pre-trained language models wins the QE shared task at WMT19. |
“A Little is Enough”: Few-Shot Quality Estimation based Corpus Filtering improves Machine Translation (2023.findings-acl)
Copied to clipboard
| Challenge: | Quality Estimation (QE) is the task of evaluating the quality of a translation when reference translation is unavailable. |
| Approach: | They propose a Quality Estimation based Filtering approach to extract high-quality parallel data from the pseudo-parallel corpus. |
| Outcome: | The proposed approach improves the machine translation system performance by up to 1.8 BLEU points over the baseline model. |
Knowledge Distillation for Quality Estimation (2021.findings-acl)
Copied to clipboard
Amit Gajbhiye, Marina Fomicheva, Fernando Alva-Manchego, Frédéric Blain, Abiola Obamuyide, Nikolaos Aletras, Lucia Specia
| Challenge: | Recent success in Quality Estimation stems from the use of multilingual pre-trained models, where large models lead to impressive results. |
| Approach: | They propose to transfer knowledge from a strong QE teacher model to a much smaller model with a different, shallower architecture. |
| Outcome: | The proposed model performs better than distilled models with 8x fewer parameters. |
A Multi-task Learning Framework for Quality Estimation (2023.findings-acl)
Copied to clipboard
Sourabh Deoghare, Paramveer Choudhary, Diptesh Kanojia, Tharindu Ranasinghe, Pushpak Bhattacharyya, Constantin Orăsan
| Challenge: | Conventional approaches to QE involve training separate models at different levels of granularity viz., word-level, sentence-level and document-level . |
| Approach: | They propose to train a single model for sentence-level and word-level QE tasks in a multi-task learning framework and compare them to baseline models. |
| Outcome: | The proposed model improves on the single-pair, multi-patch, and zero-shot settings. |
Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications (2021.emnlp-main)
Copied to clipboard
| Challenge: | Sentence-level Quality estimation (QE) is traditionally a regression task . but large multilingual contextualized language models are expensive and infeasible for real-world applications. |
| Approach: | They evaluate several model compression techniques for QE and find they are inefficient . they argue that a full model parameterization is required to achieve SoTA results . |
| Outcome: | The proposed models are poorly expressive in a regression task, the authors argue . they show that reframing QE as a classification problem and evaluating models would improve their performance in real-world applications. |
Unsupervised Quality Estimation for Neural Machine Translation (2020.tacl-1)
Copied to clipboard
Marina Fomicheva, Shuo Sun, Lisa Yankovskaya, Frédéric Blain, Francisco Guzmán, Mark Fishel, Nikolaos Aletras, Vishrav Chaudhary, Lucia Specia
| Challenge: | Existing approaches require large amounts of expert annotated data, computation, and time for training. |
| Approach: | They propose an unsupervised approach to QE where no training is required . they use a dataset that enables work on both black-box and glass-box approaches . |
| Outcome: | The proposed approach rivals state-of-the-art supervised QE models in terms of correlation with human judgments of quality. |
Self-Supervised Quality Estimation for Machine Translation (2021.emnlp-main)
Copied to clipboard
Yuanhang Zheng, Zhixing Tan, Meng Zhang, Mieradilijiang Maimaiti, Huanbo Luan, Maosong Sun, Qun Liu, Yang Liu
| Challenge: | Training QE models require massive parallel data with hand-crafted quality annotations, which are time-consuming and labor-intensive to obtain. |
| Approach: | They propose a self-supervised method to evaluate machine-translated sentences without references by recovering masked target words. |
| Outcome: | The proposed method outperforms previous unsupervised methods on several QE tasks in different language pairs and domains. |
Coursera Corpus Mining and Multistage Fine-Tuning for Improving Lectures Translation (2020.lrec-1)
Copied to clipboard
| Challenge: | Lectures translation is a case of spoken language translation and there is nil available corpus for this purpose. |
| Approach: | They propose a framework for mining a parallel corpus from publicly available lectures at Coursera . they use machine translation and cosine similarity over continuous-space sentence representations to determine sentence alignments . |
| Outcome: | The proposed framework improves translation performance when used with out-of-domain parallel corpora . it also addresses noise in the mined data, and creates high-quality evaluation splits . |
Improving Contrastive Learning of Sentence Embeddings with Focal InfoNCE (2023.findings-emnlp)
Copied to clipboard
| Challenge: | SimCSE does not fully exploit the potential of hard negative samples in contrastive learning. |
| Approach: | They propose an unsupervised contrastive learning framework that combines SimCSE with hard negative mining to enhance the quality of sentence embeddings. |
| Outcome: | The proposed framework improves sentence embeddings on various STS benchmarks in terms of Spearman’s correlation, representation alignment and uniformity. |