Papers with neural MT
Empirical Evaluation of Active Learning Techniques for Neural MT (D19-61)
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| Challenge: | Several active learning (AL) algorithms for machine translation (MT) have been well-studied for phrase-based MT. |
| Approach: | They propose to use a phrase-based algorithm to compare different AL methods in a simulated AL framework to demonstrate how unsupervised pre-training and paraphrastic embedding can be used to improve existing AL methods. |
| Outcome: | The proposed method outperforms existing methods in the context of phrase-based MT and is based on a simulated phrase-driven dataset. |
Tulun: Transparent and Adaptable Low-resource Machine Translation (2025.acl-demo)
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| Challenge: | a low-resource language that is the lingua franca in Timor-Leste lacks available corpora in the health domain. |
| Approach: | They propose a solution that combines neural MT with large language model-based post-editing guided by existing glossaries and translation memories. |
| Outcome: | The proposed system outperforms both standalone MT and LLM approaches across six low-resource languages on the FLORES dataset. |
Can Cognate Prediction Be Modelled as a Low-Resource Machine Translation Task? (2021.findings-acl)
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| Challenge: | Existing work on cognate prediction based on similarities of two languages has not studied their differences or optimized architectural choices. |
| Approach: | They compare statistical and neural MT architectures to a bilingual setup to test their hypothesis . they use monolingual pretraining, backtranslation and multilinguality to test the hypothesis based on the results . |
| Outcome: | The proposed architectures can be used to generate cognates in a given language . the proposed architecture can be employed with monolingual pretraining, backtranslation and multilinguality . |
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. |
Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation (2021.eacl-main)
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| Challenge: | Existing studies have shown that existing models amplify biases observed in training data. |
| Approach: | They propose to use MT and NLP to amplify biases observed in training data to investigate how bias amplification might affect language in a broader sense. |
| Outcome: | The proposed model amplifys biases observed in training data and could lead to an artificially impoverished language, the authors show. |
deepQuest: A Framework for Neural-based Quality Estimation (C18-1)
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| Challenge: | Predicting Machine Translation (MT) quality has been limited to word and sentence-level prediction. |
| Approach: | They propose a framework that can generalize neural QE approaches to the level of documents. |
| Outcome: | The proposed framework outperforms state-of-the-art approaches on document-level quality estimates and is 40 times faster to train. |
Document Sub-structure in Neural Machine Translation (2020.lrec-1)
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| Challenge: | Current approaches to machine translation (MT) translate sentences in isolation, disregarding context they appear in, or model context at the level of the full document. |
| Approach: | They propose to include information about the topic of the section within which each sentence is found in a document that is not homogeneous . they use a cache-based model to model the context of the document, instead of translating sentences in isolation . |
| Outcome: | The proposed model incorporates information about the topic of the section within which each sentence is found into a neural model. |
Measuring the Effect of Disfluency in Multilingual Knowledge Probing Benchmarks (2025.emnlp-main)
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| Challenge: | Several datasets have been created for factual knowledge assessment of LLMs . MLAMA uses templated sentences that do not take into account grammatical and semantic information of named entities . |
| Approach: | They use MLAMA to prompt LLMs for factual knowledge using template translations . this leads to numerous instances of ungrammaticality or wrong wording of the final prompts - authors say . |
| Outcome: | The results show that the MLAMA dataset has higher knowledge retrieval scores than previous versions. |
Benchmarking Machine Translation with Cultural Awareness (2024.findings-emnlp)
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| Challenge: | Existing studies on terminology translation focus on breaking language barriers rather than cultural barriers. |
| Approach: | They propose a parallel corpus enriched with CSI annotations in 6 language pairs for investigating Cultural-Aware Machine Translation. |
| Outcome: | The proposed corpus is enriched with CSI annotations in 6 languages and measures translation quality. |
Neural Machine Translation Quality and Post-Editing Performance (2021.emnlp-main)
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| Challenge: | a recent study has shown that MT post-editing can reduce translation quality and speed . a large-scale study involving 30 professional translators examined the relationship between MT performance and post-edited outputs. |
| Approach: | They examine the relationship between MT performance and post-editing time and quality . they use neural MT of high quality to improve translation quality based on phrase-based MT . |
| Outcome: | The proposed model is not stable predictor of time or quality, the authors say . they find that better MT systems lead to fewer changes in the sentences . |