BLASER 2.0: a metric for evaluation and quality estimation of massively multilingual speech and text translation (2024.findings-emnlp)
Copied to clipboard
| 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. |
Similar Papers
BLASER: A Text-Free Speech-to-Speech Translation Evaluation Metric (2023.acl-long)
Copied to clipboard
Mingda Chen, Paul-Ambroise Duquenne, Pierre Andrews, Justine Kao, Alexandre Mourachko, Holger Schwenk, Marta R. Costa-jussà
| Challenge: | End-to-End speech-to speech translation is generally evaluated with text-based metrics . this means generated speech has to be automatically transcribed, making the evaluation dependent on ASR systems. |
| Approach: | They propose a text-free evaluation metric for end-to-end speech-tospeech translation, named BLASER, to avoid the dependency on automatic speech recognition systems. |
| Outcome: | The proposed metric avoids the dependency on automatic speech recognition systems by encoding generated speech segments into a shared embedding space. |
Can Automatic Metrics Assess High-Quality Translations? (2024.emnlp-main)
Copied to clipboard
| 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 . |
DEMETR: Diagnosing Evaluation Metrics for Translation (2022.emnlp-main)
Copied to clipboard
| 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 . |
Multi-Dimensional Machine Translation Evaluation: Model Evaluation and Resource for Korean (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing studies on MT evaluation characterize quality of output with a single number . a recent advancement in MT technologies has enabled higher-quality, more nuanced translations . |
| Approach: | They propose a 1200-sentence MQM evaluation benchmark for English-Korean and a reference-free QE setup to evaluate the quality of the translations. |
| Outcome: | The proposed model outperforms the existing model in style and accuracy. |
Metric for Automatic Machine Translation Evaluation based on Universal Sentence Representations (N18-4)
Copied to clipboard
| Challenge: | Sentence representations can capture information that cannot be captured by local features based on character or word Ngrams. |
| Approach: | They propose a supervised regression model using universal sentence representations capable of capturing information that cannot be captured by local features based on character or word Ngrams. |
| Outcome: | The proposed model achieves state-of-the-art performance with only sentence representation features . |
Tangled up in BLEU: Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metrics (2020.acl-main)
Copied to clipboard
| 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. |
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022.emnlp-demos)
Copied to clipboard
Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina Mcmillan-major, Anna Shvets, Ashish Upadhyay, Bernd Bohnet, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez Beltrachini, Leonardo F . R. Ribeiro, Lewis Tunstall, Li Zhang, Mahim Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou
| Challenge: | Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work. |
| Approach: | They propose to use the Generation, Evaluation, and Metrics Benchmark to integrate new evaluation methods into existing evaluations. |
| Outcome: | The proposed evaluation infrastructure bridges the gap between the advantages of leaderboards and in-depth and evolving evaluations by allowing model developers to benefit from each other's work. |
Recovered in Translation: Efficient Pipeline for Automated Translation of Benchmarks and Datasets (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing benchmarks suffer from semantic drift and context loss, which can lead to misleading performance metrics. |
| Approach: | They propose a fully automated framework to enable translation of large language models . they propose to use universal self-improvement and multi-round ranking methods to improve translation quality . |
| Outcome: | The proposed framework surpasses existing benchmarks in eight languages and improves translation quality across multilingual domains. |
Revisiting Metric Reliability for Fine-grained Evaluation of Machine Translation and Summarization in Indian Languages (2026.acl-long)
Copied to clipboard
| Challenge: | Existing metrics have been developed and validated for English and other languages . this narrow focus leaves Indian languages largely overlooked, casting doubt on universality of current evaluation practices. |
| Approach: | They propose a large-scale benchmark that compares 26 automatic metrics with human judgments across six major Indian languages. |
| Outcome: | ITEM evaluates alignment of 26 automatic metrics with human judgments across six languages . authors: outliers exert significant impact on metric-human agreement, improve fidelity . they say the results offer critical guidance for advancing metric design and evaluation in Indian languages - a global market for machine translation and text summarization systems. |
Assessing Reference-Free Peer Evaluation for Machine Translation (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing methods to evaluate machine translation output are based on comparing MT output to one or more reference translations. |
| Approach: | They propose to use probabilities given by a large, multilingual model as a reference-free metric. |
| Outcome: | The proposed model is robust and likely to offer reasonable performance across a broad spectrum of domains and different system qualities. |