12 citations · 23 across the 6 of their papers we have counts for
11 papers
Towards Explainable Evaluation Metrics for Machine Translation
Christoph Leiter, Piyawat Lertvittayakumjorn, Marina Fomicheva +3
Unlike classical lexical overlap metrics such as BLEU, most current evaluation metrics for machine translation (for example, COMET or BERTScore) are based on black-box large langua…
Reducing Hallucinations in Neural Machine Translation with Feature Attribution
Joël Tang, Marina Fomicheva, Lucia Specia
Neural conditional language generation models achieve the state-of-the-art in Neural Machine Translation (NMT) but are highly dependent on the quality of parallel training dataset.…
Towards Explainable Evaluation Metrics for Natural Language Generation
Christoph Leiter, Piyawat Lertvittayakumjorn, Marina Fomicheva +3
Unlike classical lexical overlap metrics such as BLEU, most current evaluation metrics (such as BERTScore or MoverScore) are based on black-box language models such as BERT or XLM-…
The Eval4NLP Shared Task on Explainable Quality Estimation: Overview and Results
Marina Fomicheva, Piyawat Lertvittayakumjorn, Wei Zhao +2
In this paper, we introduce the Eval4NLP-2021shared task on explainable quality estimation. Given a source-translation pair, this shared task requires not only to provide a sentenc…
Pushing the Right Buttons: Adversarial Evaluation of Quality Estimation
Diptesh Kanojia, Marina Fomicheva, Tharindu Ranasinghe +3
Current Machine Translation (MT) systems achieve very good results on a growing variety of language pairs and datasets. However, they are known to produce fluent translation output…
Translation Error Detection as Rationale Extraction
Marina Fomicheva, Lucia Specia, Nikolaos Aletras
Recent Quality Estimation (QE) models based on multilingual pre-trained representations have achieved very competitive results when predicting the overall quality of translated sen…
Knowledge Distillation for Quality Estimation
Amit Gajbhiye, Marina Fomicheva, Fernando Alva-Manchego +4
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, su…
Backtranslation Feedback Improves User Confidence in MT, Not Quality
Vilém Zouhar, Michal Novák, Matúš Žilinec +7
Translating text into a language unknown to the text's author, dubbed outbound translation, is a modern need for which the user experience has significant room for improvement, bey…
Exploring Supervised and Unsupervised Rewards in Machine Translation
Julia Ive, Zixu Wang, Marina Fomicheva +1
Reinforcement Learning (RL) is a powerful framework to address the discrepancy between loss functions used during training and the final evaluation metrics to be used at test time.…
MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset
Marina Fomicheva, Shuo Sun, Erick Fonseca +7
We present MLQE-PE, a new dataset for Machine Translation (MT) Quality Estimation (QE) and Automatic Post-Editing (APE). The dataset contains eleven language pairs, with human labe…
Unsupervised Quality Estimation for Neural Machine Translation
Marina Fomicheva, Shuo Sun, Lisa Yankovskaya +6
Quality Estimation (QE) is an important component in making Machine Translation (MT) useful in real-world applications, as it is aimed to inform the user on the quality of the MT o…