Papers with quality estimation
IntelliCAT: Intelligent Machine Translation Post-Editing with Quality Estimation and Translation Suggestion (2021.acl-demo)
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| Challenge: | Existing computer-aided translation tools require the translator to edit incorrect parts of a document, while ITP tools require fewer edits. |
| Approach: | They propose an interactive translation interface with neural models that streamline the post-editing process on machine translation output. |
| Outcome: | The proposed interface can significantly improve translation quality and a user study shows that it speeds up the post-editing process by 52.9% compared to translating from scratch. |
Early-Exit and Instant Confidence Translation Quality Estimation (2026.eacl-long)
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| Challenge: | Quality estimation models are often opaque and computationally expensive, making them impractical to be part of large-scale pipelines. |
| Approach: | They propose an uncertainty-aware quality estimation model that matches previous approaches at a fraction of their costs. |
| Outcome: | The proposed method reduces evaluation costs by 50% and improves reranking performance. |
Quality Estimation for Automatically Generated Titles of eCommerce Browse Pages (N18-3)
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| Challenge: | We are generating millions of titles using machine translation, but they are prone to errors. |
| Approach: | They propose a Random Forest model which explores hand-crafted features and new features . they also propose SNs which embed metadata and generated title in the same space . |
| Outcome: | The proposed models outperform the existing models on in-house data. |
Automatic Machine Translation Evaluation in Many Languages via Zero-Shot Paraphrasing (2020.emnlp-main)
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| Challenge: | Existing metrics for machine translation evaluation are causing the correlation between human judgments and automatic metrics to break down. |
| Approach: | They propose to train a multilingual NMT system to score machine translation output with a sequence-to-sequence paraphraser, conditioned on a human reference. |
| Outcome: | The proposed model outperforms or statistically ties with all prior metrics on the WMT 2019 segment-level shared metrics task in all languages (excluding Gujarati where the model had no training data). |
Storytelling from Structured Data and Knowledge Graphs : An NLG Perspective (P19-4)
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| Challenge: | tutorial aims to explain the basic concepts of translating structured data into natural language . Various solutions for structured data translation will be discussed . |
| Approach: | tutorial aims to cover foundational, methodological, and system development aspects of translating structured data into natural language . Various solutions starting from traditional rule based/heuristic driven and modern data-driven will be discussed . |
| Outcome: | The tutorial aims to convey challenges and nuances in structured data translation, data representation techniques, and domain adaptable solutions for translation of the data into natural language form. |
Backtranslation Feedback Improves User Confidence in MT, Not Quality (2021.naacl-main)
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Vilém Zouhar, Michal Novák, Matúš Žilinec, Ondřej Bojar, Mateo Obregón, Robin L. Hill, Frédéric Blain, Marina Fomicheva, Lucia Specia, Lisa Yankovskaya
| Challenge: | Inbound translation is a modern need for which the user experience has significant room for improvement, beyond the basic machine translation facility. |
| Approach: | They propose to provide cues that indicate the quality of MT output as well as suggest possible rephrasing of the source language. |
| Outcome: | The proposed feedback module increases user confidence in the produced translation, but not the objective quality. |
Continual Quality Estimation with Online Bayesian Meta-Learning (2021.acl-short)
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| Challenge: | Existing quality estimation models for machine translation are trained and evaluated in a static setting . however, in real-life settings, test data may differ from training data . |
| Approach: | They propose an online Bayesian meta-learning framework for continuous training of QE models that adapts to the needs of different users while being robust to distributional shifts in training and test data. |
| Outcome: | The proposed framework adapts to the needs of different users while being robust to distributional shifts in training and test data. |
Understanding and Detecting Hallucinations in Neural Machine Translation via Model Introspection (2023.tacl-1)
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| Challenge: | Neural sequence generation models produce outputs that are unrelated to the source text, and are potentially harmful, yet it remains unclear in what conditions they arise and how to mitigate their impact. |
| Approach: | They first identify internal model symptoms of hallucinations by analyzing the relative token contributions to the generation in contrastive hallucinous vs. non-hallucinated outputs generated via source perturbations. |
| Outcome: | The proposed detector outperforms both baseline models and strong classifiers on English-Chinese and German-English translation test beds. |
ChrEnTranslate: Cherokee-English Machine Translation Demo with Quality Estimation and Corrective Feedback (2021.acl-demo)
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| Challenge: | a new study shows that machine translation models can translate fragments of the source sentence but make major mistakes. |
| Approach: | They propose an online machine translation demonstration system for translation between English and an endangered language Cherokee. |
| Outcome: | The proposed system achieves state-of-the-art translation performance and improves quality estimation . the proposed system can translate between English and an endangered language Cherokee . |
Quality Estimation without Human-labeled Data (2021.eacl-main)
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Yi-Lin Tuan, Ahmed El-Kishky, Adithya Renduchintala, Vishrav Chaudhary, Francisco Guzmán, Lucia Specia
| Challenge: | Quality estimation aims to measure the quality of translated content without access to a reference translation. |
| Approach: | They propose a method that uses synthetic training data to train supervised quality estimation models. |
| Outcome: | The proposed model outperforms models trained on human-annotated data for sentence and word-level prediction. |
A Linguistically Motivated Test Suite to Semi-Automatically Evaluate German–English Machine Translation Output (2022.lrec-1)
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Vivien Macketanz, Eleftherios Avramidis, Aljoscha Burchardt, He Wang, Renlong Ai, Shushen Manakhimova, Ursula Strohriegel, Sebastian Möller, Hans Uszkoreit
| Challenge: | Using fine-grained evaluation techniques, translation outputs have become better and more fluent. |
| Approach: | They propose a fine-grained test suite for the language pair German–English . they describe the creation and implementation of the test suite in detail . |
| Outcome: | The proposed test suite is based on linguistically motivated categories and phenomena and semi-automatic evaluation is carried out with regular expressions. |
Automatic Estimation of Simultaneous Interpreter Performance (P18-2)
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| Challenge: | Existing methods to predict interpreter confidence and the adequacy of the interpreted message are lacking. |
| Approach: | They propose to extend a QE pipeline to estimate interpreter performance by using five settings in three language pairs. |
| Outcome: | The proposed method can predict interpreter confidence and adequacy over five settings in three language pairs and improves interpretation strategy and evaluation measures. |
Measuring Uncertainty in Neural Machine Translation with Similarity-Sensitive Entropy (2024.eacl-long)
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| Challenge: | Uncertainty estimation is an important diagnostic tool for statistical models. |
| Approach: | They propose to adapt similarity-sensitive Shannon entropy (S3E) for NMT by incorporating a concept borrowed from theoretical ecology. |
| Outcome: | The proposed framework improves quality estimation and named entity recall, and improves translation quality. |
SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization (2021.acl-short)
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| Challenge: | Experimental results show that SimCLS can improve existing top-performing models by a large margin. |
| Approach: | They propose a framework for abstractive summarization that is conceptually simple and empirically powerful. |
| Outcome: | The proposed framework improves the performance of top-performing models by a large margin against existing top-scoring systems. |
Computer Assisted Translation with Neural Quality Estimation and Automatic Post-Editing (2020.findings-emnlp)
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| Challenge: | Using neural machine translation to approximate human parity is difficult due to the lack of parallel training corpora. |
| Approach: | They propose an end-to-end deep learning framework for quality estimation and automatic post-editing of machine translation output. |
| Outcome: | The proposed framework achieves state-of-the-art performance on the English–German dataset and human translators can significantly expedite their post-editing processing with the model. |
SentSim: Crosslingual Semantic Evaluation of Machine Translation (2021.naacl-main)
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| Challenge: | Machine translation (MT) is currently evaluated in one of two ways: monolingually or trained crosslingually by building a supervised model to predict quality scores from human-labeled data. |
| Approach: | They propose an unsupervised model that directly compares the source and machine translated sentence using strong pretrained multilingual word and sentence representations. |
| Outcome: | The proposed model outperforms glass-box approaches to quality estimation that rely on a supervised model. |
Self-Supervised Quality Estimation for Machine Translation (2021.emnlp-main)
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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. |
Competency-Aware Neural Machine Translation: Can Machine Translation Know its Own Translation Quality? (2022.emnlp-main)
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| Challenge: | Neural machine translation models are often criticized for failures that happen without competency awareness. |
| Approach: | They propose a method that extends conventional NMT with a self-estimator to translate a source sentence and estimate its competency. |
| Outcome: | The proposed method performs on translation tasks intact and on quality estimation tasks better than existing methods. |
Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correction (2021.naacl-main)
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| Challenge: | Existing GEC models produce spurious corrections or fail to detect lots of errors. |
| Approach: | They propose a neural network for GEC quality estimation with multiple hypotheses . VERNet establishes interactions among hypothese based on reasoning graph . |
| Outcome: | The proposed model achieves state-of-the-art grammatical error detection performance and best quality estimation results on four GEC datasets. |
Improving Machine Translation with Human Feedback: An Exploration of Quality Estimation as a Reward Model (2024.naacl-long)
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| Challenge: | Existing methods to improve translation quality using human feedback have not been validated. |
| Approach: | They propose to use quality estimation to predict human preferences for feedback training . they propose to detect incorrect translations and assign a penalty term to the reward scores . |
| Outcome: | The proposed method outperforms systems using larger parallel corpora by a small amount of monolingual data. |
A Post-Editing Dataset in the Legal Domain: Do we Underestimate Neural Machine Translation Quality? (2020.lrec-1)
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Julia Ive, Lucia Specia, Sara Szoc, Tom Vanallemeersch, Joachim Van den Bogaert, Eduardo Farah, Christine Maroti, Artur Ventura, Maxim Khalilov
| Challenge: | Current state-of-the-art in Neural Machine Translation (NMT) has reached remarkable progress, but human evaluations are often judged as having lower quality than top NMT systems. |
| Approach: | They propose to use a machine translation dataset with post-edited high-quality neural machine translation and independent human references to compare the results. |
| Outcome: | The proposed dataset includes 31K tuples including a source sentence, the respective machine translation by a neural machine translation system, and a post-edited version of such translation by professional translator. |
QUAK: A Synthetic Quality Estimation Dataset for Korean-English Neural Machine Translation (2022.coling-1)
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| Challenge: | despite its high utility, there are limitations concerning manual QE data creation. |
| Approach: | They propose to generate a Korean-English QE dataset that is fully automatic . they find that the algorithm is more accurate and faster than manual QE . |
| Outcome: | The proposed datasets show that they scale up to 1.58M and 6.58M, respectively, and show that the results are significantly better when compared to the previous datasets. |
Automatic Input Rewriting Improves Translation with Large Language Models (2025.naacl-long)
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| Challenge: | LLMs can rewrite inputs but in machine translation, they are primarily used to re-write outputs via post-editing. |
| Approach: | They propose to use LLMs to rewrite inputs automatically to improve machine translation (MT) they propose to simplify inputs and use quality estimation to assess translatability. |
| Outcome: | The proposed methods can be improved by using quality estimation to assess translatability. |
Improving NMT Models by Retrofitting Quality Estimators into Trainable Energy Loss (2025.coling-main)
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| Challenge: | Reinforcement learning has shown great promise in aligning language models with human preferences in a variety of text generation tasks, including machine translation. |
| Approach: | They propose a method that employs quality estimators as trainable loss networks to backpropagate to the NMT model. |
| Outcome: | The proposed method outperforms strong baselines and proximal policy optimizations on English-to-Mongolian translation. |
Are we Estimating or Guesstimating Translation Quality? (2020.acl-main)
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| 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 fine-grained error analysis of NMT, SMT and RBMT output for English-to-Dutch (L18-1)
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| Challenge: | Since 2016, the landscape of automated translation has substantially changed with the arrival of neural machine translation (NMT). |
| Approach: | They propose to use an annotated SCATE corpus of MT errors to enrich the SCATE error taxonomy to fit the neural MT output. |
| Outcome: | The proposed system outperforms phrase-based and rule-based systems except for lexical issues. |
Physician Detection of Clinical Harm in Machine Translation: Quality Estimation Aids in Reliance and Backtranslation Identifies Critical Errors (2023.emnlp-main)
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| Challenge: | a major challenge in the practical use of Machine Translation (MT) is that users lack information on translation quality to make informed decisions about how to rely on outputs. |
| Approach: | They evaluate quality estimation feedback in vivo with a human study in a medical setting. |
| Outcome: | The proposed method improves appropriate reliance on MT, but backtranslation helps detect harmful errors. |
Optimal Transport for Unsupervised Hallucination Detection in Neural Machine Translation (2023.acl-long)
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| Challenge: | Neural machine translation models can unpredictably produce severely pathological translations, known as hallucinations, that seriously undermine user trust. |
| Approach: | They propose a fully unsupervised, plug-in detector that can be used with any attention-based NMT model. |
| Outcome: | The proposed detector outperforms existing models and is competitive with detectors that employ external models trained on millions of samples. |
Outbound Translation User Interface Ptakopět: A Pilot Study (2020.lrec-1)
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| Challenge: | a task called outbound translation is not uncommon for Internet users to have to produce a text in a foreign language they have very little knowledge of and are unable to verify the translation quality. |
| Approach: | They propose an open-source modular system to inspect human interaction with machine translation systems enhanced with additional subsystems such as backward translation and quality estimation. |
| Outcome: | The proposed system is able to produce a text in a foreign language with minimal knowledge and is compared with MT systems of mid-range quality. |
AskQE: Question Answering as Automatic Evaluation for Machine Translation (2025.findings-acl)
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| Challenge: | Existing MT error detection and quality estimation (QE) techniques do not address this practical scenario. |
| Approach: | They propose a question generation and answering framework that detects critical MT errors and provides actionable feedback to help users decide whether to accept or reject MT outputs even without the knowledge of the target language. |
| Outcome: | The proposed framework has higher Kendall’s Tau correlation and decision accuracy with human ratings compared to other QE metrics. |
BLASER 2.0: a metric for evaluation and quality estimation of massively multilingual speech and text translation (2024.findings-emnlp)
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| Challenge: | Automatic evaluation of machine translation (MT) is difficult because of the number of possible ways to express a thought in a language. |
| Approach: | They propose to use BLASER 2.0 to evaluate machine translation quality . they propose to apply the reference-based model to a sentence-based version . |
| Outcome: | The proposed model is applicable to detecting translation hallucinations and filtering training datasets to obtain more reliable translation models. |
SpeechQE: Estimating the Quality of Direct Speech Translation (2024.emnlp-main)
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| Challenge: | Recent advances in automatic quality estimation for machine translation focus on written language, leaving the speech modality underexplored. |
| Approach: | They propose a new quality estimation system based on cascaded and end-to-end architectures. |
| Outcome: | The proposed system is better suited to estimating the quality of direct speech translation than existing systems designed for text translation. |
Translate Smart, not Hard: Cascaded Translation Systems with Quality-Aware Deferral (2025.emnlp-main)
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| Challenge: | Existing quality estimation metrics are used to design effective deferral rules for machine translation. |
| Approach: | They propose a simple yet effective approach for machine translation using existing quality estimation metrics as deferral rules. |
| Outcome: | The proposed approach outperforms existing models in large translation tasks while reducing computational costs. |
Diversity Explains Inference Scaling Laws: Through a Case Study of Minimum Bayes Risk Decoding (2025.acl-long)
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| Challenge: | Existing studies show that MBR decoding improves model generation performance . however, the theoretical underpinnings of these results remain uncertain . |
| Approach: | They propose a theoretical interpretation of MBR decoding from the perspective of bias–diversity decomposition. |
| Outcome: | The proposed method improves the quality estimation of hypotheses by decomposing bias and diversity into two main factors. |
Transfer Fine-tuning for Quality Estimation of Text Simplification (2024.lrec-main)
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| Challenge: | Experimental results show that quality estimation of text simplification models can be improved on a small labeled corpus. |
| Approach: | They propose a method to train quality estimation of text simplification on a small-scale labeled corpus prior to fine-tuning pre-trained language models. |
| Outcome: | The proposed method improves quality estimation of text simplification on a small-scale labeled corpus. |