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Found 29 papers, 11 papers with code
Bias Mitigation in Machine Translation Quality Estimation
Machine Translation Quality Estimation (QE) aims to build predictive models to assess the quality of machine-generated translations in the absence of reference translations.
deepQuest-py: Large and Distilled Models for Quality Estimation
We introduce deepQuest-py, a framework for training and evaluation of large and light-weight models for Quality Estimation (QE).
Bayesian Model-Agnostic Meta-Learning with Matrix-Valued Kernels for Quality Estimation
To address these challenges, we propose a Bayesian meta-learning approach for adapting QE models to the needs and preferences of each user with limited supervision.
Findings of the WMT 2021 Shared Task on Quality Estimation
We report the results of the WMT 2021 shared task on Quality Estimation, where the challenge is to predict the quality of the output of neural machine translation systems at the word and sentence levels.
BERGAMOT-LATTE Submissions for the WMT20 Quality Estimation Shared Task
We explore (a) a black-box approach to QE based on pre-trained representations; and (b) glass-box approaches that leverage various indicators that can be extracted from the neural MT systems.
Findings of the WMT 2020 Shared Task on Quality Estimation
We report the results of the WMT20 shared task on Quality Estimation, where the challenge is to predict the quality of the output of neural machine translation systems at the word, sentence and document levels.
Towards Explainable Evaluation Metrics for Machine Translation
In this context, we also discuss the latest state-of-the-art approaches to explainable metrics based on generative models such as ChatGPT and GPT4.
Reducing Hallucinations in Neural Machine Translation with Feature Attribution
We present a case study focusing on model understanding and regularisation to reduce hallucinations in NMT.
Towards Explainable Evaluation Metrics for Natural Language Generation
We also provide a synthesizing overview over recent approaches for explainable machine translation metrics and discuss how they relate to those goals and properties.
The Eval4NLP Shared Task on Explainable Quality Estimation: Overview and Results
In this paper, we introduce the Eval4NLP-2021shared task on explainable quality estimation.
Pushing the Right Buttons: Adversarial Evaluation of Quality Estimation
However, this ability is yet to be tested in the current evaluation practices, where QE systems are assessed only in terms of their correlation with human judgements.
Translation Error Detection as Rationale Extraction
Recent Quality Estimation (QE) models based on multilingual pre-trained representations have achieved very competitive results when predicting the overall quality of translated sentences.
Continual Quality Estimation with Online Bayesian Meta-Learning
Most current quality estimation (QE) models for machine translation are trained and evaluated in a static setting where training and test data are assumed to be from a fixed distribution.
Knowledge Distillation for Quality Estimation
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 conversations.
Backtranslation Feedback Improves User Confidence in MT, Not Quality
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, beyond the basic machine translation facility.
Exploring Supervised and Unsupervised Rewards in Machine Translation
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.
An Exploratory Study on Multilingual Quality Estimation
Predicting the quality of machine translation has traditionally been addressed with language-specific models, under the assumption that the quality label distribution or linguistic features exhibit traits that are not shared across languages.
MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset
We present MLQE-PE, a new dataset for Machine Translation (MT) Quality Estimation (QE) and Automatic Post-Editing (APE).
Exploring Model Consensus to Generate Translation Paraphrases
This paper describes our submission to the 2020 Duolingo Shared Task on Simultaneous Translation And Paraphrase for Language Education (STAPLE).
Multi-Hypothesis Machine Translation Evaluation
Reliably evaluating Machine Translation (MT) through automated metrics is a long-standing problem.
Unsupervised Quality Estimation for Neural Machine Translation
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 output at test time.
Taking MT Evaluation Metrics to Extremes: Beyond Correlation with Human Judgments
Much work has been dedicated to the improvement of evaluation metrics to achieve a higher correlation with human judgments.
MAJE Submission to the WMT2018 Shared Task on Parallel Corpus Filtering
This paper describes the participation of Webinterpret in the shared task on parallel corpus filtering at the Third Conference on Machine Translation (WMT 2018).
Using Contextual Information for Machine Translation Evaluation
Automatic evaluation of Machine Translation (MT) is typically approached by measuring similarity between the candidate MT and a human reference translation.
Boosting the creation of a treebank
In this paper we present the results of an ongoing experiment of bootstrapping a Treebank for Catalan by using a Dependency Parser trained with Spanish sentences.
