Challenge: a method for automatic extraction of bilingual multiword units (BMWUs) from a parallel corpus has been shown to be useful for estimating human translation quality.
Approach: They applied a method for automatic extraction of bilingual multiword units from a parallel corpus in order to investigate their contribution to translation quality in terms of adequacy and fluency.
Outcome: The method is based on generalized additive modelling and it shows that normalized BMWU ratios can be useful for estimating human translation quality.

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Are we Estimating or Guesstimating Translation Quality? (2020.acl-main)

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Challenge: A carefully engineered ensemble of pre-trained multilingual language models won the QE shared task at WMT19.
Approach: They propose to use pre-trained multilingual language models to train quality estimation for machine translation.
Outcome: A carefully engineered ensemble of pre-trained language models wins the QE shared task at WMT19.
Quality Beyond A Glance: Revealing Large Quality Differences Between Web-Crawled Parallel Corpora (2025.coling-main)

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Challenge: Parallel corpora play a vital role in advanced multilingual natural language processing tasks, notably in machine translation (MT).
Approach: They manually and automatically evaluated four well-known publicly available parallel corpora across eleven language pairs.
Outcome: The results show that the four well-known parallel corpora have a substantial amount of noisy sentence pairs, while CCMatrix and CCAligned have low quality sentences.
Cross-lingual Terminology Extraction for Translation Quality Estimation (L18-1)

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Challenge: Using common statistical measures for termhood and unithood, we identify terms from monolingual texts and investigate the contribution of terminology to translation quality.
Approach: They propose to use common statistical measures for termhood and unithood as features to train classifiers for identifying terms in cross-domain and cross-language settings.
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Understanding Cross-Lingual Alignment—A Survey (2024.findings-acl)

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Challenge: Cross-lingual alignment is the meaningful similarity of representations across languages in multilingual language models.
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On Evaluating Multilingual Compositional Generalization with Translated Datasets (2023.acl-long)

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Challenge: a growing amount of research investigating compositional generalization in NLP is done on English . a critical semantic distortion is a limitation of the translation of datasets .
Approach: They propose to translate a dataset for evaluating compositional generalization in semantic parsing.
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Quality Does Matter: A Detailed Look at the Quality and Utility of Web-Mined Parallel Corpora (2024.eacl-long)

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Challenge: Existing web-mined corpora for low-resource languages have serious quality issues, especially for lowresource language pairs.
Approach: They ranked each corpus according to a similarity measure and evaluated different portions of this ranked corpus.
Outcome: The results show that the quality of web-mined corpora for low-resource languages is significantly different from human-curated corporats.
An Empirical Study on the Robustness of Massively Multilingual Neural Machine Translation (2024.lrec-main)

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Challenge: Recent years have witnessed that massively multilingual neural machine translation (MMNMT) achieves a remarkable progress in both high- and low-resource language translation.
Approach: They propose to use a robustness evaluation benchmark dataset to assess the translation robustness of Indonesian-Chinese translation in the face of various naturally occurring noise.
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How Good Are LLMs for Literary Translation, Really? Literary Translation Evaluation with Humans and LLMs (2025.naacl-long)

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Challenge: Recent research has focused on literary machine translation (MT) but evaluation of literary MT remains an open problem.
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Fine-Tuning Large Language Models to Translate: Will a Touch of Noisy Data in Misaligned Languages Suffice? (2024.emnlp-main)

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Challenge: Traditionally, success in multilingual machine translation depends on large volume, diverse directions, and high quality of training data.
Approach: They revisit the importance of large language models for translation by fine-tuning on 32 parallel sentences.
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Word Alignment by Fine-tuning Embeddings on Parallel Corpora (2021.eacl-main)

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Challenge: Existing work on word alignment has focused on unsupervised learning on parallel text.
Approach: They propose to combine pre-trained contextualized word embeddings with multilingually trained language models to achieve competitive results on word alignment tasks.
Outcome: The proposed model outperforms state-of-the-art models on five language pairs and can train multilingual word aligners that can obtain robust performance on different language pairs.

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