Markus Freitag
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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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Mitigating metric bias in minimum bayes risk decoding
Geza Kovacs
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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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
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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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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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There's no Data Like Better Data: Using QE Metrics for MT Data Filtering
Jan-Thorsten Peter
Mara Finkelstein
Jurik Juraska
Proceedings of the Eighth Conference on Machine Translation, Association for Computational Linguistics, Singapore (2023), pp. 561-577
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Quality Estimation (QE), the evaluation of machine translation output without the need of explicit references, has seen big improvements in the last years with the use of neural metrics. In this paper we analyze the viability of using QE metrics for filtering out bad quality sentence pairs in the training data of neural machine translation systems (NMT). While most corpus filtering methods are focused on detecting noisy examples in collections of texts, usually huge amounts of web crawled data, QE models are trained to discriminate more fine-grained quality differences. We show that by selecting the highest quality sentence pairs in the training data, we can improve translation quality while reducing the training size by half. We also provide a detailed analysis of the filtering results, which highlights the differences between both approaches.
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Prompting PaLM for Translation: Assessing Strategies and Performance
Jiaming Luo
Viresh Ratnakar
George Foster
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, Toronto, Canada (2023), 15406–15427
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Large language models (LLMs) that have been trained on multilingual but not parallel text exhibit a remarkable ability to translate between languages. We probe this ability in an in-depth study of the pathways language model (PaLM), which has demonstrated the strongest machine translation (MT) performance among similarly-trained LLMs to date. We investigate various strategies for choosing translation examples for few-shot prompting, concluding that example quality is the most important factor. Using optimized prompts, we revisit previous assessments of PaLM’s MT capabilities with more recent test sets, modern MT metrics, and human evaluation, and find that its performance, while impressive, still lags that of state-of-the-art supervised systems. We conclude by providing an analysis of PaLM’s MT output which reveals some interesting properties and prospects for future work.
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