Papers with machine translation system
Filtering and Mining Parallel Data in a Joint Multilingual Space (P18-2)
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| Challenge: | Using a cosine distance in a joint multilingual sentence embedding, we filter out noisy parallel data and mine for bitexts in large news collections. |
| Approach: | They propose to learn a joint multilingual sentence embedding and use the distance between sentences in different languages to filter noisy parallel data and to mine for parallel data in large monolingual texts. |
| Outcome: | The proposed approach improves a competitive baseline on the WMT'14 task by 0.3 BLEU by filtering out 25% of the training data. |
A Large Automatically-Acquired All-Words List of Multiword Expressions Scored for Compositionality (L18-1)
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| Challenge: | Existing literature on semantically idiosyncratic multiword expressions is limited to English . idiomatic expressions are phraseological units consisting of more than one lexeme and exhibit some kind of idiom. |
| Approach: | They propose to make available a large automatically-acquired all-words list of English multiword expressions scored for compositionality. |
| Outcome: | The proposed list improves the BLEU scores of the English multiword expressions. |
Different Speech Translation Models Encode and Translate Speaker Gender Differently (2025.acl-short)
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| Challenge: | Recent studies on interpreting the hidden states of speech models have shown their ability to capture speaker-specific features, including gender. |
| Approach: | They propose to use probing methods to assess gender encoding across ST models. |
| Outcome: | The proposed models capture speaker-specific features, including gender, while older models do not . low gender encoding capabilities result in systems’ tendency toward a masculine default, a translation bias that is more pronounced in newer architectures. |
Empirical Analysis of Noising Scheme based Synthetic Data Generation for Automatic Post-editing (2022.lrec-1)
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| Challenge: | Automatic post-editing (APE) is a research field that aims to correct errors in translated sentences regardless of the utilized machine translation system. |
| Approach: | They propose a method for automatically generating APE data based on a noising scheme from a parallel corpus. |
| Outcome: | The proposed method shows that depending on the type of noise, the noising scheme-based APE data generation may lead to inferior performance. |
Vecalign: Improved Sentence Alignment in Linear Time and Space (D19-1)
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| Challenge: | Sentence-aligned bitext is used to train nearly all machine translation systems. |
| Approach: | They propose a bilingual sentence alignment method which is linear in time and space with respect to the number of sentences being aligned. |
| Outcome: | The proposed method outperforms the existing method by 5 F1 points on a German–French test set and improves downstream MT quality by 1.7 and 1.6 BLEU in Sinhala-English and Nepali-English, respectively. |
A Bayesian Optimization Approach to Machine Translation Reranking (2025.naacl-long)
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| Challenge: | reranking is a method of improving prediction quality but can add computational cost. |
| Approach: | They propose to score a list of prediction candidates with an external scoring model and return the highest-scoring candidate. |
| Outcome: | The proposed method achieves the same CometKiwi score using 70 evaluations on average compared to scoring a subset of 180 candidates. |
A Tulu Resource for Machine Translation (2024.lrec-main)
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| Challenge: | Using parallel datasets, we train a machine translation system in English–Tulu . |
| Approach: | They present a parallel dataset for English–Tulu translation using human translations into the multilingual machine translation resource FLORES-200. |
| Outcome: | The proposed model outperforms Google Translate by 19 BLEU points (in September 2023). |
Detecting Various Types of Noise for Neural Machine Translation (2022.findings-acl)
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| Challenge: | a recent study investigated the impact of noise on the performance of machine translation systems. |
| Approach: | They propose to combine recent research on data filtering with original analysis . they find that most of the suggested noise types can be detected with 90% accuracy . |
| Outcome: | The proposed filtering systems can detect noise types with 90% accuracy in high resource settings. |
Charles Translator: A Machine Translation System between Ukrainian and Czech (2024.lrec-main)
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Martin Popel, Lucie Polakova, Michal Novák, Jindřich Helcl, Jindřich Libovický, Pavel Straňák, Tomas Krabac, Jaroslava Hlavacova, Mariia Anisimova, Tereza Chlanova
| Challenge: | a system for translating between Ukrainian and Czech was developed in the spring of 2022 . the system was not available at the time in the required quality . |
| Approach: | They propose a machine translation system between Ukrainian and Czech to reduce the impact of the Russian-Ukrainian war on individuals and society. |
| Outcome: | The proposed system translates directly between Ukrainian and Czech, compared to other systems that use English as a pivot. |
Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translation (2025.findings-acl)
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Yingfeng Luo, Tong Zheng, Yongyu Mu, Bei Li, Qinghong Zhang, Yongqi Gao, Ziqiang Xu, Peinan Feng, Xiaoqian Liu, Tong Xiao, JingBo Zhu
| Challenge: | Recent advances in machine translation have focused on a single pre-trained decoder . encoder-decoder architectures have received relatively little attention in NMT . |
| Approach: | They propose a method that leverages LLMs as MT encoders and pairs them with lightweight decoders to develop universal translation models. |
| Outcome: | The proposed method matches or surpasses baselines in terms of translation quality but achieves 75% reduction in memory footprint of the KV cache. |
Bilingual Lexicon Induction through Unsupervised Machine Translation (P19-1)
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| Challenge: | Existing methods for bilingual lexicon induction use nearest neighbor or related retrieval methods to induce word translation pairs. |
| Approach: | They propose a method that aligns word embeddings in two languages and uses them to build a phrase-table and a language model to extract the bilingual lexicon. |
| Outcome: | The proposed method improves accuracy 6 points over nearest neighbor and 4 points over CSLS retrieval on the same cross-lingual embeddings. |
Translation Memories as Baselines for Low-Resource Machine Translation (2022.lrec-1)
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| Challenge: | low-resource machine translation research often requires building baselines to benchmark progress in translation quality. |
| Approach: | They argue that using available text as a translation memory baseline is simple and effective . they say that if you have parallel text, you have a TM . |
| Outcome: | a new study shows that using available text as a translation memory baseline is simple and effective . low-resource machine translation is often of too low quality to use directly, the authors argue . |
IndiGEC: Multilingual Grammar Error Correction for Low-Resource Indian Languages (2025.emnlp-main)
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| Challenge: | despite growing interest in GEC, most research has focused on English due to the lack of benchmark datasets for low-resource lan-guages. |
| Approach: | They propose a new approach to generate high-quality synthetic data for GEC using monolingual corpora. |
| Outcome: | The proposed framework outperforms other monolingual methods in English, Hindi, Bengali, Marathi, and Tamil. |