BLEURT Has Universal Translations: An Analysis of Automatic Metrics by Minimum Risk Training (2023.acl-long)
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| Challenge: | Automated metrics are used for machine translation, but they are often considered to be black boxes with potential biases that are difficult to detect. |
| Approach: | They analyze automatic metrics from the perspective of their guidance for machine translation training. |
| Outcome: | The proposed measures improve the performance of machine translation models. |
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Robustness Tests for Automatic Machine Translation Metrics with Adversarial Attacks (2023.findings-emnlp)
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| Challenge: | BERTScore, BLEURT, and COMET are automatic evaluation metrics that are often underperformed on adversarially-synthesized texts. |
| Approach: | They examine MT evaluation metric performance on adversarially-synthesized texts . they validate that automatic metrics tend to overpenalize adversarial-degraded translations . |
| Outcome: | The results show that automatic metrics tend to overpenalize adversarially-degraded translations. |
Tangled up in BLEU: Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metrics (2020.acl-main)
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| Challenge: | Existing methods for judging metrics are sensitive to the translations used for evaluation, leading to falsely confident conclusions about a metric’s efficacy. |
| Approach: | They propose a method for thresholding performance improvement under an automatic metric against human judgements by using a pairwise system ranking method. |
| Outcome: | The proposed method allows quantification of type I versus type II errors incurred, i.e., insignificant human differences in system quality that are accepted, and significant human differences that are rejected. |
BLEURT: Learning Robust Metrics for Text Generation (2020.acl-main)
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| Challenge: | Text generation has made significant advances, but evaluation metrics have lagged behind. |
| Approach: | They propose a learning evaluation metric for English based on BERT . BLEURT can model human judgment with a few thousand possibly biased training examples . |
| Outcome: | The proposed model can model human judgment with a few thousand potentially biased training examples. |
Identifying Weaknesses in Machine Translation Metrics Through Minimum Bayes Risk Decoding: A Case Study for COMET (2022.aacl-main)
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| Challenge: | Neural metrics have a high correlation with human judgements but they are hard to eliminate due to their "black box" nature. |
| Approach: | They propose to use minimum bayes risk decoding to explore and quantify weaknesses in COMET models. |
| Outcome: | The proposed model is not sensitive enough to discrepancies in numbers and named entities, and is hard to remove by training on additional synthetic data. |
Can Automatic Metrics Assess High-Quality Translations? (2024.emnlp-main)
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| Challenge: | a recent human evaluation study found that translations produced by current MT systems achieve very high-quality scores when judged by humans on a direct assessment scale of 0 to 100. |
| Approach: | They stress-test the ability of current translation quality metrics to detect correct translations . they show that current metrics often over or underestimate translation quality . |
| Outcome: | The proposed method overestimates translation quality, the authors show . they show that current metrics often overestimate translation quality . |
MENLI: Robust Evaluation Metrics from Natural Language Inference (2023.tacl-1)
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| Challenge: | Recent proposed BERT-based evaluation metrics for text generation are vulnerable to adversarial attacks, e.g., relating to information correctness. |
| Approach: | They propose to use BERT-based evaluation metrics for text generation to evaluate text for semantic similarity but are vulnerable to adversarial attacks using Natural Language Inference. |
| Outcome: | The proposed metrics outperform existing summarization metrics but perform below SOTA MT metrics on standard benchmarks. |
DEMETR: Diagnosing Evaluation Metrics for Translation (2022.emnlp-main)
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| Challenge: | BLEU scores are based on string overlap, but they are opaque in comparison to newer learned metrics. |
| Approach: | They propose a dataset to evaluate MT evaluation metrics based on linguistic perturbations in English . they find learned metrics perform substantially better than string-based metrics . |
| Outcome: | The proposed dataset shows that learned metrics perform better than string-based metrics . the dataset contains 31K English examples that cover 35 different linguistic phenomena . |
BERTTune: Fine-Tuning Neural Machine Translation with BERTScore (2021.acl-short)
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| Challenge: | Neural machine translation models are biased toward limited translation references . BERTScore is a scoring function based on contextual embeddings that overcomes the limitations of n-gram-based metrics. |
| Approach: | They propose to fine-tune models with a new evaluation metric based on contextual embeddings to overcome the limitations of n-gram-based metrics. |
| Outcome: | The proposed training objective improves translations that are different from the translations but close in the contextual embedding space. |
A Novel Metric for Measuring the Robustness of Large Language Models in Non-adversarial Scenarios (2024.findings-emnlp)
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| Challenge: | Using large language models, we evaluated their robustness on multiple datasets. |
| Approach: | They propose a new metric for assessing model robustness by empirical evaluation of several models on multiple datasets. |
| Outcome: | The proposed metric is based on a set of datasets that are constructed by introducing naturally-occurring, non-malicious perturbations or by generating semantically equivalent paraphrases of input questions or statements. |
Not All Errors are Equal: Learning Text Generation Metrics using Stratified Error Synthesis (2022.findings-emnlp)
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| Challenge: | Existing learning metrics are limited to tasks where large human ratings are available. |
| Approach: | They propose a model-based natural language generation (NLG) evaluation metric that is highly correlated with human judgements without requiring human annotation. |
| Outcome: | The proposed metric outperforms all prior unsupervised metrics on multiple NLG tasks including translation, image captioning, and WebNLG text generation. |