Markus Freitag
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Are LLMs Breaking MT Metrics? Results of the WMT24 Metrics Shared Task
Nitika Mathur
Chi-kiu Lo
Eleftherios Avramidis
Ricardo Rei
Brian Thompson
Frédéric Blain
Tom Kocmi
Jiayi Wang
David Adelani
Marianna Buchicchio
Chrysoula Zerva
Alon Lavie
2024
Mitigating metric bias in minimum bayes risk decoding
Proceedings of the Ninth Conference on Machine Translation (2024), pp. 1063-1094
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Minimum bayes risk decoding has been shown to improve translation quality both on automated metrics and human evaluations. In this paper we show that MBR decoding tends to show larger improvements in the utility metric and similar metrics, compared to other unrelated metrics. To mitigate this metric bias issue, we explore using MBR decoding using ensembles of multiple metrics as the utility function, as well as QE filtering followed by MBR decoding. Human evaluations show that using an ensemble of metrics improves quality over MBR or QE decoding with a single metric.
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Collecting high-quality translations is crucial for the development and evaluation of machine translation systems. However, traditional human-only approaches are costly and slow. This study presents a comprehensive investigation of 11 approaches for acquiring translation data, including human-only, machine-only, and hybrid approaches. Our findings demonstrate that human-machine collaboration can match or even exceed the quality of human-only translations, while being more cost-efficient. Error analysis reveals the complementary strengths between human and machine contributions, highlighting the effectiveness of collaborative methods. Cost analysis further demonstrates the economic benefits of human-machine collaboration methods, with some approaches achieving top-tier quality at around 60% of the cost of traditional methods. We release a publicly available dataset containing nearly 18,000 segments of varying translation quality with corresponding human ratings to facilitate future research.
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Findings of the WMT24 General Machine Translation Shared Task: The LLM Era is Here but MT is Not Solved Yet
Tom Kocmi
Eleftherios Avramidis
Rachel Bawden
Ondrej Bojar
Anton Dvorkovich
Christian Federman
Mark Fishel
Thamme Gowda
Roman Grundkiewicz
Barry Haddow
Marzena Karpinska
Philipp Koehn
Benjamin Marie
Christof Monz
Kenton Murray
Masaaki Nagata
Martin Popel
Maja Popovic
Mariya Shmatova
Steinþór Steingrímsson
Vilém Zouhar
2024
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This overview paper presents the results of the General Machine Translation Task organised as part of the 2024 Conference on Machine Translation (WMT). In the general MT task, participants were asked to build machine translation systems for any of 11 language pairs, to be evaluated on test sets consisting of three to five different domains. In addition to participating systems, we collected translations from 8 different large language models (LLMs) and 4 online translation providers. We evaluate system outputs with professional human annotators using a new protocol called Error Span Annotations (ESA).
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Reliable human evaluation is critical to the development of successful natural language generation models, but achieving it is notoriously difficult. Stability is a crucial requirement when ranking systems by quality: consistent ranking of systems across repeated evaluations is not just desirable, but essential. Without it, there is no reliable foundation for hill-climbing or product launch decisions. In this paper, we use machine translation and its state-of-the-art human evaluation framework, MQM, as a case study to understand how to set up reliable human evaluations that yield stable conclusions. We investigate the optimal configurations for item allocation to raters, number of ratings per item, and score normalization. Our study on two language pairs provides concrete recommendations for designing replicable human evaluation studies. We also collect and release the largest publicly available dataset of multi-segment translations rated by multiple professional translators, consisting of nearly 140,000 segment annotations across two language pairs.
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Recent research in neural machine translation (NMT) has shown that training on high-quality machine-generated data can outperform training on human-generated data. This work accompanies the first-ever release of a LLM-generated, MBR-decoded and QE-reranked dataset with both sentence-level and multi-sentence examples. We perform extensive experiments to demonstrate the quality of our dataset in terms of its downstream impact on NMT model performance. We find that training from scratch on our (machine-generated) dataset outperforms training on the (web-crawled) WMT’23 training dataset (which is 300 times larger), and also outperforms training on the top-quality subset of the WMT’23 training dataset. We also find that performing self-distillation by finetuning the LLM which generated this dataset outperforms the LLM’s strong few-shot baseline. These findings corroborate the quality of our dataset, and demonstrate the value of high-quality machine-generated data in improving performance of NMT models.
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WMT23 Metrics shared task Submission: Quality Estimation using Minimum Bayes Risk
Subhajit Naskar
Proceedings of the Eighth Conference on Machine Translation, Association for Computational Linguistics, Singapore (2023), pp. 806-811
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This report describes the Minimum Bayes Risk Quality Estimation (MBR-QE) submission to the Workshop on Machine Translation's 2023 Metrics Shared Task. MBR decoding with neural utility metrics (BLEURT) are known to be very effective in generating high quality machine translations. We use the underlying assumption of MBR decoding and develop a MBR based reference-free quality estimation metric. Our method uses a evaluator machine translation system and a reference-based utility metric (BLEURT, MeticX) to calculate a quality estimation score of a model. We report results related to comparing different MBR configuration and utility metrics.
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MetricX-23: The Google Submission to the WMT 2023 Metrics Shared Task
Jurik Juraska
Mara Finkelstein
Aditya Siddhant
Mahdi Mirzazadeh
Conference on Machine Translation (2023)
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This report details the MetricX-23 submission to the Workshop on Machine Translation's 2023 Metrics Shared Task and provides an overview of the experiments that informed which metrics were submitted. Our three submissions---each with a quality estimation (or reference-free) version---are all learned regression-based metrics that vary in the data used for training and which pretrained language model was used for initialization. We report results related to understanding (1) which supervised training data to use, (2) the impact of how the training labels are normalized, (3) the amount of synthetic training data to use, (4) how metric performance is related to model size, and (5) the effect of initializing the metrics with different pretrained language models. The training recipes that we found to be most successful are detailed in this report.
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Epsilon Sampling Rocks: Investigating Sampling Strategies for Minimum Bayes Risk Decoding for Machine Translation
Behrooz Ghorbani
Patrick Fernandes
Findings of the Association for Computational Linguistics: EMNLP 2023, Association for Computational Linguistics, Singapore, pp. 9198-9209
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Recent advances in machine translation (MT) have shown that Minimum Bayes Risk (MBR) decoding can be a powerful alternative to beam search decoding, especially when combined with neural-based utility functions. However, the performance of MBR decoding depends heavily on how and how many candidates are sampled from the model. In this paper, we explore how different sampling approaches for generating candidate lists for MBR decoding affect performance. We evaluate popular sampling approaches, such as ancestral, nucleus, and top-k sampling. Based on our insights into their limitations, we experiment with the recently proposed epsilon-sampling approach, which prunes away all tokens with a probability smaller than epsilon, ensuring that each token in a sample receives a fair probability mass. Through extensive human evaluations, we demonstrate that MBR decoding based on epsilon-sampling significantly outperforms not only beam search decoding, but also MBR decoding with all other tested sampling methods across four language pairs.
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Ties Matter: Meta-Evaluating Modern Metrics with Pairwise Accuracy and Tie Calibration
George Foster
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Singapore, pp. 12914-12929
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Kendall's tau is frequently used to meta-evaluate how well machine translation (MT) evaluation metrics score individual translations. Its focus on pairwise score comparisons is intuitive but raises the question of how ties should be handled, a gray area that has motivated different variants in the literature. We demonstrate that, in settings like modern MT meta-evaluation, existing variants have weaknesses arising from their handling of ties, and in some situations can even be gamed. We propose instead to meta-evaluate metrics with a version of pairwise accuracy that gives metrics credit for correctly predicting ties, in combination with a tie calibration procedure that automatically introduces ties into metric scores, enabling fair comparison between metrics that do and do not predict ties. We argue and provide experimental evidence that these modifications lead to fairer ranking-based assessments of metric performance.
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