Markus Freitag
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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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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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The Devil is in the Errors: Leveraging Large Language Models for Fine-grained Machine Translation Evaluation
André Martins
Graham Neubig
Ankush Garg
Patrick Fernandes
Mara Finkelstein
Conference on Machine Translation (2023)
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Automatic evaluation of machine translation (MT) is a critical tool driving the rapid iterative development of MT systems. While considerable progress has been made on direct estimation of quality scores, the resulting metrics lack the informativeness of more detailed schemes that annotate individual errors, such as Multidimensional Quality Metrics (MQM). In this paper, we fill this gap by proposing \textbf{\textsc{AutoMQM}}, a prompting technique which leverages the \textit{reasoning} and \textit{in-context learning} capabilities of large language models (LLMs) and asks them to identify and categorize errors in translations. We start by evaluating recent LLMs, such as PaLM and PaLM-2, through simple \textit{score prediction} prompting, and we study the impact of labeled data through in-context learning and finetuning. We then evaluate \textsc{AutoMQM} with PaLM-2 models, and we find that it improves performance compared to just prompting for scores (with particularly large gains for larger models) while providing interpretability through error spans that align with human annotations.
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INSTRUCTSCORE: Towards Explainable Text Generation Evaluation with Automatic Feedback
Liangming Pan
William Wang
Lei Li
Danqing Wang
Zhenqiao Song
Wenda Xu
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Singapore, pp. 5967-5994
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Automatically evaluating the quality of language generation is critical. Although recent learned metrics show high correlation with human judgement, these metrics do not provide explicit explanation of their verdict, nor associate the scores with defects in the generated text. To address this limitation, we present INSTRUCTSCORE, a fine-grained explainable evaluation metric for text generation. By harnessing both explicit human instruction and the implicit knowledge of GPT-4, we fine-tune a text evaluation metric based on LLaMA, producing both a score for generated text and a human readable diagnostic report. We evaluate INSTRUCTSCORE on a variety of generation tasks, including translation, captioning, data-to-text, and commonsense generation. Experiments show that our 7B model surpasses all other unsupervised metrics, including those based on 175B GPT-3 and GPT-4. Surprisingly, our INSTRUCTSCORE, even without direct supervision from human-rated data, achieves performance levels on par with state-of-the-art metrics like COMET22, which were fine-tuned on human ratings.
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There's no Data Like Better Data: Using QE Metrics for MT Data Filtering
Jurik Juraska
Jan-Thorsten Peter
Mara Finkelstein
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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Training and Meta-Evaluating Machine Translation Evaluation Metrics at the Paragraph-Level
Jurik Juraska
Mara Finkelstein
Proceedings of the Eighth Conference on Machine Translation, Association for Computational Linguistics, Singapore (2023), pp. 996-1013
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As research on machine translation moves to translating text beyond the sentence level, it remains unclear how effective automatic evaluation metrics are at scoring longer translations. In this work, we first propose a method for creating paragraph-level data for training and meta-evaluating metrics from existing sentence-level data. Then, we use these new datasets to benchmark existing sentence-level metrics as well as train learned metrics at the paragraph level. Interestingly, our experimental results demonstrate that using sentence-level metrics to score entire paragraphs is equally as effective as using a metric designed to work at the paragraph level. We speculate this result can be attributed to properties of the task of reference-based evaluation as well as limitations of our datasets with respect to capturing all types of phenomena that occur in paragraph-level translations.
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Prompting PaLM for Translation: Assessing Strategies and Performance
George Foster
Viresh Ratnakar
Jiaming Luo
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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MetricX-23: The Google Submission to the WMT 2023 Metrics Shared Task
Mahdi Mirzazadeh
Jurik Juraska
Mara Finkelstein
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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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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