Statistical Power and Translationese in Machine Translation Evaluation (2020.emnlp-main)
Copied to clipboard
| Challenge: | a recent paper argues that translationese has been used to describe features of translated text . a translationed text can be more explicit than the original source, authors say . authors recommend reverse-created test data be omitted from future evaluations . |
| Approach: | They propose to omit translationese from future machine translation evaluations . they also re-evaluate a past evaluation claiming human-parity of MT . |
| Outcome: | The proposed analysis shows that translationese does not affect machine translation evaluations. |
Similar Papers
Original or Translated? A Causal Analysis of the Impact of Translationese on Machine Translation Performance (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing work on translationese neglects important factors and conclusions are mostly correlational but not causal. |
| Approach: | They use a dataset where MT training data are also labeled with human translation directions to examine the impact of translationese on machine translation evaluation. |
| Outcome: | The proposed model learns in the same direction as human translation directions. |
Assessing Human-Parity in Machine Translation on the Segment Level (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Recent machine translation shared tasks have shown top-performing systems to tie or outperform human translation. |
| Approach: | They examine the outputs of top-performing systems in a recent machine translation shared task . they find that some systems outperform human translation on average . |
| Outcome: | a new method identifies segments for which human and machine perform poorly . the results show that top-performing systems outperform human translation on average . |
On “Human Parity” and “Super Human Performance” in Machine Translation Evaluation (2022.lrec-1)
Copied to clipboard
| Challenge: | In this paper, we reassess claims of human parity and super human performance in machine translation. |
| Approach: | They reassess claims of human parity and super human performance in machine translation . they argue that human translation involves much more than what is embedded in automatic systems . |
| Outcome: | The proposed results show that human translation involves much more than what is embedded in automatic systems. |
Revisiting Machine Translation for Cross-lingual Classification (2023.emnlp-main)
Copied to clipboard
| Challenge: | Recent work in cross-lingual learning has pivoted around multilingual models, which are typically pretrained on unlabeled corpora in multiple languages using some form of language modeling objective. |
| Approach: | They propose to use a stronger machine translation system to mitigat mismatch between training on original text and running inference on machine translated text. |
| Outcome: | The proposed approach is highly task dependent and calls into question the dominance of multilingual models for cross-lingual classification. |
Lost in Translation, and Found: Detecting and Interpreting Translation Effects (2026.acl-long)
Copied to clipboard
Shira Wein, Anna Serbina, Jiyuan Ji, Nathan Wolf, Jason DeGraaff, Prajakta Kini, Maria Leonor Pacheco
| Challenge: | Translationese refers to the statistical patterns that distinguish translated texts from original texts. |
| Approach: | They analyze linguistic features which enable our model to achieve high accuracy by a collection of linguistic characteristics and pretrained neural models pick up these features without any fine-tuning. |
| Outcome: | The proposed model achieves high accuracy with a set of linguistic features that correspond to translationese theories and pretrained neural models pick up these features without any fine-tuning. |
Scientific Credibility of Machine Translation Research: A Meta-Evaluation of 769 Papers (2021.acl-long)
Copied to clipboard
| Challenge: | a meta-evaluation of machine translation (MT) has been conducted in 769 research papers . a recent study shows that evaluation practices have changed over the past decade . |
| Approach: | They propose a meta-evaluation method for machine translation that uses BLEU scores to evaluate MT performance. |
| Outcome: | The proposed meta-evaluation of machine translation shows that evaluation practices have changed over the past decade . the authors suggest that the evaluation process should be streamlined and standardized to ensure the validity of the evaluation method . |
Upping the Ante: Towards a Better Benchmark for Chinese-to-English Machine Translation (L18-1)
Copied to clipboard
| Challenge: | Currently, there is no widely accepted standard for evaluation of machine translation (MT) for Chinese-to-English translation, there are no standard for standardized training sets, development sets, and test sets. |
| Approach: | They propose to use Chinese-to-English machine translation as a benchmark . they build a highly competitive state-of-the-art MT system that outperforms reported results . |
| Outcome: | The proposed system outperforms reported results on NIST OpenMT test sets in almost all papers published in major conferences and journals in computational linguistics and artificial intelligence in the past 11 years. |
Lost in the Source Language: How Large Language Models Evaluate the Quality of Machine Translation (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent studies have shown that Large Language Models (LLMs) can be used as translation evaluators. |
| Approach: | They propose to use both coarse-grained and fine-grounded prompts to discern the utility of source versus reference data in machine translation evaluation tasks. |
| Outcome: | The proposed model can be used to evaluate translations in multiple languages. |
Refined Assessment for Translation Evaluation: Rethinking Machine Translation Evaluation in the Era of Human-Level Systems (2025.findings-emnlp)
Copied to clipboard
Dmitry Popov, Vladislav Negodin, Ekaterina Enikeeva, Iana Matrosova, Nikolay Karpachev, Max Ryabinin
| Challenge: | Currently, traditional evaluation methods struggle to detect subtle translation errors. |
| Approach: | They propose to use a dataset of human evaluations for English–Russian translations created by professional linguists to enable consistent and rich annotation. |
| Outcome: | The proposed protocol allows expert assessments without time pressure to yield substantially different results from standard evaluations. |
Informative Manual Evaluation of Machine Translation Output (2020.coling-main)
Copied to clipboard
| Challenge: | a new method for manual evaluation of machine translation output is proposed . evaluators mark problematic parts of the translated text, not just overall scores . |
| Approach: | They propose a method for manual evaluation of machine translation output based on marking actual issues in the translated text. |
| Outcome: | The proposed method can be applied on any genre/domain and language pair . it can be guided by various types of quality criteria and can be used for other types of generated text. |