Papers with MT
Feriji: A French-Zarma Parallel Corpus, Glossary & Translator (2024.acl-srw)
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| Challenge: | MT has seen significant advances in recent years, but the representation of African languages in MT systems is underrepresented due to linguistic complexities and limited resources. |
| Approach: | They propose a first robust parallel French-Zarma corpus and a glossary for MT that contains 61,085 sentences in Zarma and 42,789 in French. |
| Outcome: | The proposed model improves the representation of the Zarma language, a dialect of Songhay, spoken by over 5 million people across Niger and neighboring countries. |
kNN-BOX: A Unified Framework for Nearest Neighbor Generation (2024.eacl-demo)
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| Challenge: | kNN-BOX enables quick development and visualization for novel generation paradigm . Currently, knn-BOx has provided implementation of seven popular kN-MT variants . |
| Approach: | They propose a framework which decomposes the datastore-augmentation approach into three modules . they apply kNN-BOX to machine translation and three other tasks . |
| Outcome: | The proposed framework decomposes the datastore-augmentation approach into three modules . it provides implementation of seven popular kNN-MT variants, covering research from performance enhancement to efficiency optimization. |
TransIns: Document Translation with Markup Reinsertion (2021.emnlp-demo)
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| Challenge: | MT models cannot translate complex formatted documents, as markup can be nested, apply to spans contiguous in source but non-contiguous. |
| Approach: | They propose a system for non-plain text document translation that reinserts markup into translated sentences using token alignments between source and target sentences. |
| Outcome: | The proposed system outperforms translation services in terms of markup quality . it integrates token alignments between source and target sentences to reinsert markup . the proposed system is available under the MIT license . |
ESCAPE: a Large-scale Synthetic Corpus for Automatic Post-Editing (L18-1)
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| Challenge: | eSCAPE is the largest freely-available Synthetic Corpus for Automatic Post-Editing released so far. |
| Approach: | a team of researchers develops a Synthetic Corpus for Automatic Post-Editing . eSCAPE is the largest freely-available Synthetic corpus for automatic post-editing released so far . the results prove that the models always improve MT quality with statistically significant gains . |
| Outcome: | eSCAPE is the largest freely-available Synthetic Corpus for Automatic Post-Editing released so far. |
Mitigating Hallucinations and Off-target Machine Translation with Source-Contrastive and Language-Contrastive Decoding (2024.eacl-short)
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| Challenge: | Hallucinations and off-target translations remain unsolved problems in machine translation, especially for low-resource languages and massively multilingual models. |
| Approach: | They propose two methods to mitigate hallucinations and off-target translations with a modified decoding objective without retraining or external models. |
| Outcome: | The proposed methods reduce translation errors with segment-level chrF2 below 10 by 67-83% on average across 57 tested translation directions. |
Difficulty-Aware Machine Translation Evaluation (2021.acl-short)
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| Challenge: | Current MT evaluation measures pay the same attention to each sentence component . in real-world examinations, the questions vary in difficulty and weightings . |
| Approach: | They propose a difficulty-aware MT evaluation metric that takes translation difficulty into account . they propose to use this metric to evaluate machine translation (MT) results . |
| Outcome: | The proposed method outperforms most MT evaluation metrics in terms of human correlation. |
Machine Translation for Low-Resource Languages through Monolingual Data and LLM: A Case Study of English-to-Basque (2026.eacl-srw)
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| Challenge: | Existing LLMs do not translate well from English to Basque, but they yield an acceptable performance in the reverse direction. |
| Approach: | They propose to use a Basque monolingual corpora to train an LLM-based MT system . they use 'sovereignty fine tuning' to generate parallel corporata, and then use preference optimization . |
| Outcome: | The proposed system improves translation quality in English-to-Basque direction while requiring limited data for low-resource languages. |
Unsupervised Cross-Lingual Representation Learning (P19-4)
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| Challenge: | a comprehensive survey of cutting-edge weakly-supervised and unsupervised cross-lingual word representations is presented . |
| Approach: | This tutorial provides a comprehensive survey of recent work on weakly-supervised and unsupervised cross-lingual word representations. |
| Outcome: | This tutorial provides a comprehensive survey of cutting-edge weakly-supervised and unsupervised word representations. |
EffEval: A Comprehensive Evaluation of Efficiency for MT Evaluation Metrics (2023.findings-emnlp)
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| Challenge: | a recent surge of interest in developing evaluation metrics based on pretrained large language models (LLMs) can better cope with lexical variation. |
| Approach: | They propose to replace computation-intensive transformers with lighter alternatives and employ linear and quadratic approximations for alignment algorithms on top of LLM representations. |
| Outcome: | The proposed approach replaces computation-intensive transformers with lighter alternatives and employs linear and quadratic approximations for alignment algorithms on top of LLM representations. |
Simulated multiple reference training improves low-resource machine translation (2020.emnlp-main)
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| Challenge: | Existing valid translations for a given sentence are limited by a single reference translation, causing data sparsity in low-resource settings. |
| Approach: | They propose a method that approximates the full space of possible translations by sampling a paraphrase of the reference sentence from a MT model and training it to predict the paraphraser’s distribution over possible tokens. |
| Outcome: | The proposed method improves in low-resource settings and is complementary to back-translation. |
The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation (P18-1)
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Mia Xu Chen, Orhan Firat, Ankur Bapna, Melvin Johnson, Wolfgang Macherey, George Foster, Llion Jones, Mike Schuster, Noam Shazeer, Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Zhifeng Chen, Yonghui Wu, Macduff Hughes
| Challenge: | In recent years, the emergence of seq2seq models has revolutionized the field of machine translation by replacing traditional phrase-based approaches with neural machine translation (NMT) systems based on the encoder-decoder paradigm. |
| Approach: | They propose to use a convolutional seq2seq model to combine the strengths of the two approaches. |
| Outcome: | The proposed architectures outperform the existing models on the WMT’14 benchmark dataset. |
MT-Telescope: An interactive platform for contrastive evaluation of MT systems (2021.acl-demo)
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| Challenge: | MT-Telescope is an open source, written in Python, and is built around a user friendly and dynamic web interface. |
| Approach: | They propose a platform to facilitate comparative analysis of the output quality of two Machine Translation (MT) systems. |
| Outcome: | The proposed platform supports fine-grained segment-level analysis and interactive visualisations that expose the fundamental differences in the performance of the compared systems. |
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. |
Topic-guided Example Selection for Domain Adaptation in LLM-based Machine Translation (2024.eacl-srw)
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| Challenge: | Current machine translation (MT) systems perform well in domains on which they were trained, but adaptation to unseen domains remains a challenge. |
| Approach: | They propose to use large language models to adapt to unseen domains by in-context example selection. |
| Outcome: | The proposed method outperforms baselines on multilingual out-of-domain tests, though it does not match performance with strong baselines for the in-language setting. |
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. |
Data Filtering using Cross-Lingual Word Embeddings (2021.naacl-main)
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| Challenge: | varying task definitions and data conditions make it difficult to draw a meaningful comparison. |
| Approach: | They propose to use language identification to perform data filtering on MT data based on cross-lingual word embeddings to identify weaknesses in language identification tool. |
| Outcome: | The proposed methods perform well on three real-life, high resource MT tasks while performing weakly within more realistic task conditions. |
Machine Translation into Low-resource Language Varieties (2021.acl-short)
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| Challenge: | Current machine translation systems generate a "standard" target language, but many languages have multiple varieties that are different from the standard language. |
| Approach: | They propose a framework to rapidly adapt machine translation systems to generate different target varieties . they propose to use no parallel data to generate languages close to, but different from, the standard target language . |
| Outcome: | The proposed model improves on a system that generates Ukrainian and Belarusian in two languages with no parallel data. |
Machine Translation of Restaurant Reviews: New Corpus for Domain Adaptation and Robustness (D19-56)
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Alexandre Berard, Ioan Calapodescu, Marc Dymetman, Claude Roux, Jean-Luc Meunier, Vassilina Nikoulina
| Challenge: | BLEU: MT is a very robust and efficient way to translate user-generated content. |
| Approach: | They propose a task to encourage research on MT robustness and domain adaptation . they ask professionals to translate 11.5k french 4SQ reviews to English . |
| Outcome: | The proposed task improves on the existing MT systems in a real-world scenario . the proposed methods improve translation accuracy and sentiment analysis . |
Selecting Machine-Translated Data for Quick Bootstrapping of a Natural Language Understanding System (N18-3)
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| Challenge: | In recent years, there has been growing interest in voice-controlled devices, such as Amazon Alexa or Google home. |
| Approach: | They investigate the use of Machine Translation to bootstrap a natural language understanding system for a new language for the use case of a large-scale voice-controlled device. |
| Outcome: | The proposed method reduces the time and cost of getting annotated corpus for a new language while still providing a large enough coverage of user requests. |
CLIReval: Evaluating Machine Translation as a Cross-Lingual Information Retrieval Task (2020.acl-demos)
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| Challenge: | evaluating machine translation (MT) with cross-lingual information retrieval is relatively time-consuming and subjective. |
| Approach: | They propose a toolkit that evaluates machine translation with a proxy task of cross-lingual information retrieval. |
| Outcome: | The proposed toolkit is based on the "metrics shared task" of WMT2019. |
Alligators All Around: Mitigating Lexical Confusion in Low-resource Machine Translation (2025.naacl-short)
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| Challenge: | Current machine translation systems for low-resource languages have a particular failure mode: they tend to confuse words within a domain. |
| Approach: | They propose a recall-based metric to measure the failure mode of machine translation systems for low-resource languages. |
| Outcome: | The proposed model outperforms a lexicon-based translator in 122 low-resource languages. |
Towards Speech to Speech Machine Translation focusing on Indian Languages (2023.eacl-demo)
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| Challenge: | SSMT is a web application for translating videos from one language to another by cascading multiple language modules. |
| Approach: | They introduce an SSMT pipeline for translating videos from one language to another by cascading multiple language modules. |
| Outcome: | The proposed system can get 3.5+ MOS score for English to Hindi using human intervention. |
Efficient Neural Machine Translation for Low-Resource Languages via Exploiting Related Languages (2020.acl-srw)
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| Challenge: | Neural Machine Translation (NMT) is a rapidly advancing MT paradigm that can be used to improve machine translation for many languages. |
| Approach: | They propose a technique called Unified Transliteration and Subword Segmentation to leverage language similarity while exploiting parallel data from related languages. |
| Outcome: | The proposed approach improves translation accuracy by 5 BLEU points over the standard Transformer-based NMT models. |
What about “em”? How Commercial Machine Translation Fails to Handle (Neo-)Pronouns (2023.acl-long)
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| Challenge: | Wrong pronoun translations can discriminate against marginalized groups, e.g., non-binary individuals. |
| Approach: | They compare 3rd-person pronoun translations to five other languages . they propose to address gender exclusivity in future research . |
| Outcome: | The proposed method compares translations of gendered vs. gender-neutral pronouns from english to five other languages and vice versa. |
CharSpan: Utilizing Lexical Similarity to Enable Zero-Shot Machine Translation for Extremely Low-resource Languages (2024.eacl-short)
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| Challenge: | Existing models for ELRLs lack parallel corpora and monolingual corporata . authors propose novel character-span noise argumentation model to facilitate cross-lingual transfer . |
| Approach: | They propose a character-span noise argumentation model to facilitate cross-lingual transfer . they use character-size noise argumentations to regularize training data of HRL . |
| Outcome: | The proposed model outperforms baselines on closely related HRL-ELRL pairs from three different language families. |
Part Represents Whole: Improving the Evaluation of Machine Translation System Using Entropy Enhanced Metrics (2022.findings-aacl)
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| Challenge: | Existing machine translation metrics have poor correlations with human assessments . entropy-based evaluations are often limited to a limited number of samples . |
| Approach: | They propose a fast and unsupervised approach to enhance machine translation metrics using entropy by introducing sentence-level difficulty. |
| Outcome: | The proposed method outperforms existing metrics on five sub-tracks in the WMT19 Metrics shared tasks. |
Translation or Recitation? Calibrating Evaluation Scores for Machine Translation of Extremely Low-Resource Languages (2026.acl-short)
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| Challenge: | Existing studies show that performance across low-resource settings is variable, resulting in a significant barrier for the MT community. |
| Approach: | They propose to use FRED Difficulty Metrics to contextualize reported performance across different language pairs to determine whether breakthroughs reported in other contexts are artifacts of benchmark collection. |
| Outcome: | The proposed metrics explain a significant portion of result variability rather than model capability. |
Neural Arabic Text Diacritization: State of the Art Results and a Novel Approach for Machine Translation (D19-52)
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| Challenge: | a number of Arabic text diacritizers use diacritics to convey information about meaning of a word . Arabic text to speech (TTS) requires a complex process to determine the correct diacritical for each character . |
| Approach: | They propose to use Arabic diacritization to enhance machine translation models . they propose to build automatic Arabic text diacritics using two approaches . |
| Outcome: | The proposed models are either better or on par with other models, which require language-dependent post-processing steps, unlike ours. |
University of Edinburgh’s submission to the Document-level Generation and Translation Shared Task (D19-56)
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| Challenge: | University of Edinburgh participated in all six tracks: NLG, MT, and MT+NLG with English and German as targeted languages. |
| Approach: | The University of Edinburgh participated in all six tracks: NLG, MT, and MT+NLG . they submitted a multilingual system based on the Content Selection and Planning model . |
| Outcome: | The University of Edinburgh participated in all six tracks with English and German as target languages. |
CRPO: Confidence-Reward Driven Preference Optimization for Machine Translation (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have shown great potential in natural language processing tasks, but their application to machine translation remains challenging due to pretraining on predominantly English-centric datasets. |
| Approach: | They propose a method that combines reward scores with model confidence to improve model selection for fine-tuning. |
| Outcome: | The proposed method outperforms existing methods in translation accuracy and data efficiency. |
Naver Labs Europe’s Systems for the Document-Level Generation and Translation Task at WNGT 2019 (D19-56)
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| Challenge: | Recent advances in machine translation and natural language generation have created many challenges in this field especially when context is considered. |
| Approach: | They propose to leverage data from machine translation and natural language generation tasks to do transfer learning between MT, NLG and MT with source-side metadata. |
| Outcome: | The proposed approach outperforms the previous state-of-the-art on the Rotowire NLG task. |
EvolveMT: an Ensemble MT Engine Improving Itself with Usage Only (2023.acl-industry)
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| Challenge: | EvolveMT is a method for the efficient combination of multiple machine translation engines. |
| Approach: | They propose a method that selects the output from one engine for each segment and uses online learning techniques to predict the most appropriate system for each translation request. |
| Outcome: | The proposed method achieves similar translation accuracy at a lower cost than selecting the best translation of each segment from all translations using an MT quality estimator. |
On the differences between BERT and MT encoder spaces and how to address them in translation tasks (2021.acl-srw)
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| Challenge: | Various studies show that pretrained language models cannot replace encoders in neural machine translation despite their success in other tasks. |
| Approach: | They propose a supervised transformation from one into the other to improve the applicability of BERT in neural machine translation. |
| Outcome: | The proposed transformations show that they cannot replace encoders in MT despite their success in other tasks. |
Unsupervised Quality Estimation for Neural Machine Translation (2020.tacl-1)
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Marina Fomicheva, Shuo Sun, Lisa Yankovskaya, Frédéric Blain, Francisco Guzmán, Mark Fishel, Nikolaos Aletras, Vishrav Chaudhary, Lucia Specia
| Challenge: | Existing approaches require large amounts of expert annotated data, computation, and time for training. |
| Approach: | They propose an unsupervised approach to QE where no training is required . they use a dataset that enables work on both black-box and glass-box approaches . |
| Outcome: | The proposed approach rivals state-of-the-art supervised QE models in terms of correlation with human judgments of quality. |
When Does Translation Require Context? A Data-driven, Multilingual Exploration (2023.acl-long)
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| Challenge: | Recent studies in context-aware MT attempt to target a small set of discourse phenomena during evaluation, however not in a fully systematic way. |
| Approach: | They develop a multilingual discourse-aware benchmark to evaluate model performance on discourse phenomena in a given dataset. |
| Outcome: | The proposed model improves on previously studied phenomena while uncovering others which were not addressed. |
Automatic Correction of Human Translations (2022.naacl-main)
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| Challenge: | Despite recent advances in machine translation, a tremendous amount of translated content in the world is still written by humans. |
| Approach: | They propose a task of translation error correction (TEC) that corrects human-generated translations by correcting all errors in a source sentence and a human-created translation. |
| Outcome: | The proposed system improves translation accuracy by 5.1 points compared to MT systems with human errors . |
MMPE: A Multi-Modal Interface using Handwriting, Touch Reordering, and Speech Commands for Post-Editing Machine Translation (2020.acl-demos)
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Nico Herbig, Santanu Pal, Tim Düwel, Kalliopi Meladaki, Mahsa Monshizadeh, Vladislav Hnatovskiy, Antonio Krüger, Josef van Genabith
| Challenge: | a shift from traditional translation to post-editing (PE) of machine-translated text can save time and reduce errors, but it also affects the design of translation interfaces. |
| Approach: | They propose a prototype that combines traditional input modes with pen, touch, and speech modalities for post-editing of machine-translated (MT) they propose to use these modalités to cross out or hand-write new text, drag and drop words for reordering, or use spoken commands to update the text in place. |
| Outcome: | The proposed interfaces can be used to cross out or hand-write new text, drag and drop words for reordering, or use spoken commands to update the text in place. |
Zero-Shot vs. Translation-Based Cross-Lingual Transfer: The Case of Lexical Gaps (2024.naacl-short)
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| Challenge: | lexical gaps exist in a variety of domains, such as QA, but they can only be expressed as a combination of words in another language. |
| Approach: | They compare the current performance and long-term viability of two approaches to cross-lingual transfer . they leverage lexical gaps to create a multilingual question answering dataset . |
| Outcome: | The proposed model outperforms zero-shot transfer and machine translation (MT) lexical gaps exist in a variety of domains, including linguistics, linguistic coding, and linguistic analysis. |
A Novel Paradigm Boosting Translation Capabilities of Large Language Models (2024.findings-naacl)
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| Challenge: | Existing studies on LLMs focused on supervised fine-tuning but their effectiveness has been limited. |
| Approach: | They propose a paradigm consisting of three stages: Secondary Pre-training using extensive monolingual data, Continual Pre- training with interlinear text format documents, and Leveraging source-language consistent instruction for supervised fine-tuning. |
| Outcome: | The proposed approach surpasses previous work and achieves superior performance compared to models such as NLLB-54B(CITATION) and GPT3.5-text-davinci-003. |
Towards Zero-Shot Multimodal Machine Translation (2025.findings-naacl)
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| Challenge: | Current multimodal machine translation systems rely on fully supervised data, which is costly to collect and prevents extension of MMT to language pairs with no such data. |
| Approach: | They propose a method to bypass the need for fully supervised data to train MMT systems . they adapt a strong text-only machine translation model to a visually conditioned language model and a divergence test set to evaluate how well models use images to disambiguate translations. |
| Outcome: | The proposed method can generalize to languages with no fully supervised training data. |
Problems with Cosine as a Measure of Embedding Similarity for High Frequency Words (2022.acl-short)
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| Challenge: | We find that word similarities estimated by cosine over contextual embeddings are understated and trace this effect to training data frequency. |
| Approach: | They propose to use cosine similarity to estimate word similarities in contextual embeddings to trace this effect to training data frequency. |
| Outcome: | The proposed model underestimates similarity between frequent and low frequency words even after controlling for polysemy and other factors. |
Bootstrapping a Crosslingual Semantic Parser (2020.findings-emnlp)
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| Challenge: | Recent advances in semantic parsing are limited to English but professional translation can be prohibitively expensive. |
| Approach: | They adapt a semantic parser trained on a single language to new languages and multiple domains with minimal annotation. |
| Outcome: | The proposed approach achieves parsing accuracy within 2% of translation using only 50% of training data. |
Multilingual Denoising Pre-training for Neural Machine Translation (2020.tacl-1)
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Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer
| Challenge: | Existing approaches to pre-train models focus on only English corpora, but this is not common in machine translation. |
| Approach: | They propose a sequence-to-sequence denoising auto-encoder pre-trained on monolingual corpora . they show that it produces significant performance gains across MT tasks . |
| Outcome: | The proposed model can achieve significant performance gains across a wide variety of MT tasks. |
MTNT: A Testbed for Machine Translation of Noisy Text (D18-1)
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| Challenge: | Noisy input text can cause disastrous mistranslations in most modern machine translation systems. |
| Approach: | They propose a benchmark dataset for Machine Translation of Noisy Text (MTNT) they use reddit comments and professionally sourced translations to examine noise types. |
| Outcome: | The proposed dataset can provide an attractive testbed for noise-robust machine translation systems. |
Extreme Adaptation for Personalized Neural Machine Translation (P18-2)
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| Challenge: | Existing models that capture speaker-related variations do not include explicit information about the speaker. |
| Approach: | They propose a method that adapts the bias of the output softmax to each particular user . they propose to model speaker-related variations as an additional bias vector in the softmax layer . |
| Outcome: | The proposed technique improves translation accuracy and better reflection of speaker traits in target text. |
Gender Bias in Machine Translation (2021.tacl-1)
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| Challenge: | Interest in understanding, assessing, and mitigating gender bias in machine translation (MT) still lacks cohesion. |
| Approach: | They propose to review current conceptualizations of gender bias in machine translation (MT) they summarize previous studies and propose ways to mitigate bias. |
| Outcome: | This paper summarizes the current conceptualizations and proposes strategies to mitigate biases in machine translation (MT) . |
TRANSLATIONCORRECT: A Unified Framework for Machine Translation Post-Editing with Predictive Error Assistance (2025.acl-demo)
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| Challenge: | Current workflows for machine translation (MT) post-editing and research data collection are inefficient and time-consuming. |
| Approach: | They propose a framework that combines MT and error prediction within a single environment. |
| Outcome: | **TranslationCorrect** exports high-quality span-based annotations in the Error Span Annotation format, using an error taxonomy inspired by Multidimensional Quality Metrics (MQM). |
Unsupervised Bitext Mining and Translation via Self-Trained Contextual Embeddings (2020.tacl-1)
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| Challenge: | Existing methods to extract parallel sentences from unaligned text yield surprisingly good results. |
| Approach: | They propose an unsupervised method to create pseudo-parallel corpora for machine translation (MT) from unaligned text using multilingual BERT to create source and target sentence embeddings for nearest-neighbor search and adapt the model via self-training. |
| Outcome: | The proposed method outperforms existing methods and outperformed previous state-of-the-art methods by boosting translation performance by up to 3.5 BLEU on the WMT’14 French-English and WMT'16 German-English tasks. |
A Comparison of Transformer and Recurrent Neural Networks on Multilingual Neural Machine Translation (C18-1)
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| Challenge: | Recent studies have shown that multilingual NMT models can handle more than one translation direction with a single system. |
| Approach: | They propose a multilingual neural machine translation model that can handle more than one translation direction with a single system. |
| Outcome: | The proposed model performs well in low-resource settings against bilingual systems. |
Considerations for meaningful sign language machine translation based on glosses (2023.acl-short)
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| Challenge: | In machine translation, sign language translation based on glosses is becoming more popular . limitations of glossed approaches are not discussed in a transparent manner, and there is no common standard for evaluation. |
| Approach: | They propose to use a gloss-based approach to evaluate machine translation results . they propose to include realistic datasets, stronger baselines and convincing evaluation . |
| Outcome: | The proposed approach is based on a neural gloss translation model. |
Multilingual Verbalisation of Knowledge Graphs (2025.findings-emnlp)
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| Challenge: | Most work on Knowledge Graph (KG) verbalisation is monolingual leaving open the question of how to scale KG-to-Text generation to languages with varying amounts of resources. |
| Approach: | They explore how to scale KG-to-Text generation to languages with varying resources . they construct multilingual training data and test data for each language . |
| Outcome: | The proposed approach performs best on all 9 languages, compared with other approaches on low vs high resource languages and on in- v. out-of-domain data. |
From Disjoint Sets to Parallel Data to Train Seq2Seq Models for Sentiment Transfer (2020.findings-emnlp)
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| Challenge: | Existing methods for sentiment transfer have relied on unsupervised methods due to lack of parallel corpora. |
| Approach: | They propose a method for creating parallel data to train Seq2Seq neural networks for sentiment transfer. |
| Outcome: | The proposed method outperforms existing unsupervised methods in sentiment transfer tasks. |
Ready to Translate, Not to Represent? Bias and Performance Gaps in Multilingual LLMs Across Language Families and Domains (2026.findings-acl)
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Md. Faiyaz Abdullah Sayeedi, Subhey Sadi Rahman, Md. Mahbub Alam, Md. Adnanul Islam, Jannatul Ferdous Deepti, Tasnim Mohiuddin, Md Mofijul Islam, Swakkhar Shatabda
| Challenge: | Large Language Models (LLMs) have redefined Machine Translation, enabling context-aware and fluent translations across hundreds of languages and textual domains. |
| Approach: | They propose a framework and dataset to evaluate the translation quality and fairness of open-source LLMs. |
| Outcome: | The proposed framework and dataset evaluates translation quality and fairness of open-source LLMs. |
In-Context Example Selection via Similarity Search Improves Low-Resource Machine Translation (2025.findings-naacl)
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| Challenge: | Existing studies have shown that in-context examples for machine translation are beneficial for high-resource languages. |
| Approach: | They propose to use in-context examples for machine translation (MT) they argue that similarity-based selection can improve MT . |
| Outcome: | The proposed approach improves machine translation (MT) and low-resource languages. |
Potential Idiomatic Expression (PIE)-English: Corpus for Classes of Idioms (2022.lrec-1)
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Tosin Adewumi, Roshanak Vadoodi, Aparajita Tripathy, Konstantina Nikolaido, Foteini Liwicki, Marcus Liwicki
| Challenge: | Potential Idiomatic Expression (PIE) dataset for NLP in English contains over 20,100 samples with almost 1,200 cases of idioms from 10 classes (or senses). |
| Approach: | They present a large Potential Idiomatic Expression (PIE) dataset for Natural Language Processing (NLP) in English. |
| Outcome: | The proposed dataset contains over 20,100 samples with almost 1,200 cases of idioms (with their meanings) from 10 classes (or senses). |
On the Limitations of Unsupervised Bilingual Dictionary Induction (P18-1)
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| Challenge: | Unsupervised machine translation does not require cross-lingual supervision, whether a dictionary, translations, or comparable corpora. |
| Approach: | They propose an adversarial, unsupervised cross-lingual word embedding technique for bilingual dictionary induction that exploits a weak supervision signal from identical words. |
| Outcome: | The proposed model relies heavily on an adversarial, unsupervised cross-lingual word embedding technique for bilingual dictionary induction. |
No Error Left Behind: Multilingual Grammatical Error Correction with Pre-trained Translation Models (2024.eacl-long)
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| Challenge: | Grammatical Error Correction (GEC) research has primarily focused on English with little coverage for other languages. |
| Approach: | They propose a multilingual machine translation model that can be fine-tuned to improve error correction out-of-the-box. |
| Outcome: | The proposed model outperforms similar-sized MT5 models and competes favourably with larger models. |
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 . |
BPE vs. Morphological Segmentation: A Case Study on Machine Translation of Four Polysynthetic Languages (2022.findings-acl)
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| Challenge: | Morphologically rich polysynthetic languages present a challenge for NLP systems due to data sparsity. |
| Approach: | They propose to use subword segmentation to reduce data sparsity in polysynthetic languages . they compare supervised and unsupervised morphological segmentation methods to Byte-Pair Encodings . |
| Outcome: | The proposed methods outperform BPEs in MT tasks for all language pairs except for Nahuatl . the proposed methods are more efficient than supervised methods, but less sparse in fusional languages. |
MATO: A Model-Agnostic Training Optimization for Aspect Sentiment Triplet Extraction (2025.naacl-long)
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| Challenge: | Existing models with strong in-house performance may struggle to generalize to diverse expressions. |
| Approach: | They propose a model-agnostic t**raining method to improve ASTE model inference . they propose to compute the violation rate (VR) on each element of one triplet . |
| Outcome: | The proposed method can improve aspect sentiment triplet extraction models consistent with expected results facing triplet element diversity. |
ParCorFull2.0: a Parallel Corpus Annotated with Full Coreference (2022.lrec-1)
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| Challenge: | Existing corpus ParCorFull contains parallel texts for English-German, French and Portuguese . translation of coreference across languages is challenging for MT and other NLP applications . |
| Approach: | They describe a parallel corpus annotated with full coreference chains for multiple languages . they use the existing corpus ParCorFull to study translation of coreference across languages - a challenge for machine translation and NLP . |
| Outcome: | The proposed corpus addresses translation of coreference across languages, a problem still challenging for machine translation and other multilingual natural language processing applications. |
One Model to Learn Both: Zero Pronoun Prediction and Translation (D19-1)
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| Challenge: | Zero pronouns (ZPs) are often omitted in pro-drop languages, but should be recalled in non-pro-drop language. |
| Approach: | They propose a unified and discourse-aware ZP translation approach for neural MT models . they jointly learn to predict and translate ZPs in an end-to-end manner . |
| Outcome: | The proposed method improves translation performance and ZP prediction accuracy over baseline models and external models. |
Revisit Automatic Error Detection for Wrong and Missing Translation – A Supervised Approach (D19-1)
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| Challenge: | Current machine translation techniques are bottlenecked by adequacy issues . we propose automatic detection of missing and wrong translations . |
| Approach: | They propose automatic detection of adequacy errors in MT hypothesis for MT model evaluation by annotating missing and wrong translations in 15000 Chinese-English translation pairs. |
| Outcome: | The proposed model can detect missing and wrong translations in 15000 Chinese-English translation pairs. |
A Flexible Multi-Task Model for BERT Serving (2022.acl-short)
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| Challenge: | a proposed BERT-based multi-task framework is suitable for iterative and incremental development of the tasks. |
| Approach: | They propose an efficient BERT-based multi-task framework that is suitable for iterative and incremental development of the tasks. |
| Outcome: | The proposed framework achieves 99.6% of performance of the full fine-tuning method while reducing up to two thirds of its overhead. |
Who Needs Decoders? Efficient Estimation of Sequence-Level Attributes with Proxies (2024.eacl-long)
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| Challenge: | Autoregressive decoding is expensive for many sequence-to-sequence tasks, but for some downstream tasks, the actual decoding output is not needed, just attributes of the sequence. |
| Approach: | They propose non-autoregressive proxy models that can efficiently predict scalar-valued sequence-level attributes from the encodings, avoiding the expensive decoding stage. |
| Outcome: | The proposed models outperform ensembles in machine translation (MT) and automatic speech recognition (ASR) while being significantly faster. |
Macro-Average: Rare Types Are Important Too (2021.naacl-main)
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| Challenge: | MT metrics trained on segment-level human judgments are inherently non-transparent and reflect undesirable biases. |
| Approach: | They propose to use a type-based classifier metric to evaluate machine translation and compare it with a supervised and unsupervised one. |
| Outcome: | The proposed model outperforms other models in indicating cross-lingual information retrieval task performance and shows that it can be used to compare supervised and unsupervised neural machine translation. |
Few-shot learning through contextual data augmentation (2021.eacl-main)
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| Challenge: | Various strategies have been explored to learn from a journalist's post-edits . state-of-the-art APE systems require large numbers of post- edits for training . |
| Approach: | They propose to teach a pre-trained machine translation model to translate previously unseen words accurately . they extend a data augmentation approach to create training examples with similar contexts . |
| Outcome: | The proposed model improves accuracy on the scale of one to five examples with only 1 to 5 examples. |
A Linguistically Motivated Test Suite to Semi-Automatically Evaluate German–English Machine Translation Output (2022.lrec-1)
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Vivien Macketanz, Eleftherios Avramidis, Aljoscha Burchardt, He Wang, Renlong Ai, Shushen Manakhimova, Ursula Strohriegel, Sebastian Möller, Hans Uszkoreit
| Challenge: | Using fine-grained evaluation techniques, translation outputs have become better and more fluent. |
| Approach: | They propose a fine-grained test suite for the language pair German–English . they describe the creation and implementation of the test suite in detail . |
| Outcome: | The proposed test suite is based on linguistically motivated categories and phenomena and semi-automatic evaluation is carried out with regular expressions. |
Increasing Coverage and Precision of Textual Information in Multilingual Knowledge Graphs (2023.emnlp-main)
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| Challenge: | Existing methods to generate knowledge graphs are unable to handle non-English textual information. |
| Approach: | They propose a task of automatic Knowledge Graph Completion to bridge the gap between English and non-English textual information. |
| Outcome: | The proposed method bridges the gap between the quantity and quality of textual information between English and non-English languages. |
A Shocking Amount of the Web is Machine Translated: Insights from Multi-Way Parallelism (2024.findings-acl)
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| Challenge: | Multi-way parallel, machine generated content dominates the translations in lower resource languages . a limited investigation suggests this selection bias is the result of low quality content generated in English and translated into many lower resource language via MT. |
| Approach: | They show that multi-way parallel, machine generated content dominates translations in many languages . they also find evidence of a selection bias in the type of content which is translated into many languages. |
| Outcome: | The results suggest that the low quality of multi-way translations on the web was likely created using machine translation. |
Learning Simplifications for Specific Target Audiences (P18-2)
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| Challenge: | Text simplification is a monolingual text-to-text transformation task . data from TS data can contain multiple simplifications of the same original text . |
| Approach: | They propose to use sequence-to-sequence neural models to build models tailored for specific grade levels. |
| Outcome: | The proposed model outperforms state-of-the-art approaches for a monolingual text-to-text transformation task. |
Principled Paraphrase Generation with Parallel Corpora (2022.acl-long)
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| Challenge: | a popular method for paraphrase generation is round-trip machine translation (MT). |
| Approach: | They propose a method that formalizes the implicit similarity function and relaxes it by requiring the entire translation distribution to match . they propose MT can be used to generate paraphrases by decoding back to the source without having to generate pivot translations. |
| Outcome: | The proposed approach is more principled and efficient than round-trip machine translation (MT) and offers an adjustable parameter to control the fidelity-diversity trade-off. |
Translation of Multifaceted Data without Re-Training of Machine Translation Systems (2024.findings-emnlp)
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| Challenge: | a novel MT pipeline that considers the intra-data relation is proposed . previous MT systems have demonstrated relatively low performance, making them hardly utilized as another data source. |
| Approach: | They propose a new MT pipeline that considers the intra-data relation . they propose CS and IT to enhance the intra data relation based on a data point . |
| Outcome: | The proposed pipeline improves translation quality and training data compared with the existing approach . it yields better training data and better translation quality than previous approaches . |
Modality Adaption or Regularization? A Case Study on End-to-End Speech Translation (2023.acl-short)
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| Challenge: | End-to-end speech translation models have limited training data and are often inefficient due to the inconsistency of length and representation between speech and text. |
| Approach: | They find that the "modality gap" between speech and text data is not a major problem in E2E ST . they decouple the encoder to speech encoder and text encoder, and they find that there is a 'capacity gap' |
| Outcome: | The proposed model achieves 29.0 for en-de and 40.3 for fr on the MuST-C dataset. |
BIG-C: a Multimodal Multi-Purpose Dataset for Bemba (2023.acl-long)
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| Challenge: | Bemba is the most populous language of Zambia but lacks resources for research . despite its significance, Bemba remains under-resourced and lacking in high-quality data and resources for NLP experiments and language technologies. |
| Approach: | They propose a large multimodal dataset for Bemba that includes images, transcriptions and translations. |
| Outcome: | The proposed dataset is based on images, transcriptions and translations of Bemba speakers . it provides baselines on speech recognition, machine translation and speech translation tasks . |
Quality Estimation-Assisted Automatic Post-Editing (2023.findings-emnlp)
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| Challenge: | Existing APE and QE combination strategies have not shown significant performance gains in the field of automatic post-editing (APE). |
| Approach: | They propose to train a model on APE and QE tasks to improve the APE performance by using a multi-task learning methodology that treats both tasks as a 'bargaining game' they also investigate various existing combination strategies and show that their approach achieves state-of-the-art performance for a ‘distant’ language pair, viz., English-Marathi. |
| Outcome: | The proposed model improves on two different language pairs, viz., English-Marathi and English-German. |
Uncertainty Quantification for Evaluating Gender Bias in Machine Translation (2026.findings-eacl)
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| Challenge: | Existing models can reproduce existing social inequalities but cannot be reduced. |
| Approach: | They propose that models should maintain uncertainty when input is ambiguous to avoid reinforcing biases. |
| Outcome: | The proposed model can detect gender bias when translated to ambiguous and unambiguous sources and shows that it does not correlate with high translation accuracy and debiases the two cases differently. |
CTC Alignments Improve Autoregressive Translation (2023.eacl-main)
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Brian Yan, Siddharth Dalmia, Yosuke Higuchi, Graham Neubig, Florian Metze, Alan W Black, Shinji Watanabe
| Challenge: | Connectionist Temporal Classification (CTC) is widely used for automatic speech recognition (ASR) but lags behind attentional decoder approaches in terms of translation quality. |
| Approach: | They propose to use a CTC/attention framework to validate this hypothesis by modifying the Hybrid CTC-Attention model proposed for automatic speech recognition to support text-to-text translation (MT) and speech-totext translation. |
| Outcome: | The proposed model outperforms pure-attention baselines across six translation tasks. |
Did Translation Models Get More Robust Without Anyone Even Noticing? (2025.acl-long)
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| Challenge: | Neural machine translation models are highly sensitive to “noisy” inputs, such as spelling errors, abbreviations, and formatting issues. |
| Approach: | They revisit this insight in light of recent multilingual MT models and large language models applied to machine translation. |
| Outcome: | The proposed models perform better on clean data than previous models, but none of the open models use robustness techniques. |
Bring More Attention to Syntactic Symmetry for Automatic Postediting of High-Quality Machine Translations (2023.acl-short)
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| Challenge: | Existing APE systems are not good at handling high-quality MTs even for a language pair with abundant data resources, English–German. |
| Approach: | They propose a linguistically motivated method of regularization that encourages symmetric self-attention on the given MT. |
| Outcome: | The proposed method improves the state-of-the-art architecture’s APE quality for high-quality MTs. |
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. |
XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages (2023.findings-emnlp)
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Sebastian Ruder, Jonathan Clark, Alexander Gutkin, Mihir Kale, Min Ma, Massimo Nicosia, Shruti Rijhwani, Parker Riley, Jean-Michel Sarr, Xinyi Wang, John Wieting, Nitish Gupta, Anna Katanova, Christo Kirov, Dana Dickinson, Brian Roark, Bidisha Samanta, Connie Tao, David Adelani, Vera Axelrod, Isaac Caswell, Colin Cherry, Dan Garrette, Reeve Ingle, Melvin Johnson, Dmitry Panteleev, Partha Talukdar
| Challenge: | Existing datasets are often informed by established research directions in the NLP community. |
| Approach: | They propose a benchmark to evaluate the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks. |
| Outcome: | The proposed benchmark evaluates the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks. |
Rethinking the Word-level Quality Estimation for Machine Translation from Human Judgement (2023.findings-acl)
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| Challenge: | Word-level Quality Estimation (QE) of Machine Translation aims to detect potential translation errors in the translated sentence without reference. |
| Approach: | They propose to use a human-generated translation judgment to generate a word-level quality estimate (QE) using a translation error rate toolkit to detect translation errors without reference. |
| Outcome: | The proposed dataset is more consistent with human judgment and confirms the effectiveness of the proposed tag-correcting strategies. |
Machine Translation between Spoken Languages and Signed Languages Represented in SignWriting (2023.findings-eacl)
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| Challenge: | Yin et al. ( 2021) calls for including sign language processing (SLP) in natural language processing research. |
| Approach: | They propose to use a sign language writing system to parse, factorize, decode and evaluate signed languages. |
| Outcome: | The proposed method achieves over 30 BLEU in a bilingual setup and over 20 BLUE in two multilingual setups. |
First Tragedy, then Parse: History Repeats Itself in the New Era of Large Language Models (2024.naacl-long)
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| Challenge: | a new system trained on well over a trillion words smashes the state of the art by a margin previously thought impossible. |
| Approach: | They argue that disparities in scale are transient and researchers can work to reduce them . they argue that data, rather than hardware, is still a bottleneck for many applications . |
| Outcome: | a new system trained on well over a trillion words smashes the state of the art by a margin previously thought impossible. |
Shallow Discourse Parsing for Under-Resourced Languages: Combining Machine Translation and Annotation Projection (2020.lrec-1)
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| Challenge: | Shallow Discourse Parsing (SDP) relies on large amounts of training data, which so far exists only for English. |
| Approach: | They propose to translate an existing English Penn Discourse TreeBank into German and use it to create a German corpus annotated for shallow discourse relations in the news domain. |
| Outcome: | The proposed corpus is annotated for shallow discourse relations in the (financial) news domain. |
TQ-AutoTest – An Automated Test Suite for (Machine) Translation Quality (L18-1)
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| Challenge: | Especially the trend towards neural MT has renewed peoples' interest in better and more analytical diagnostic methods for MT quality. |
| Approach: | They propose a framework that supports a linguistic evaluation of machine translations using test suites. |
| Outcome: | The proposed framework supports linguistic evaluation of (machine) translations using test suites. |
Bilingual Dictionary Based Neural Machine Translation without Using Parallel Sentences (2020.acl-main)
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| Challenge: | a monolingual speaker can learn to translate by looking up a bilingual dictionary . a novel task of machine translation (MT) is based on no parallel sentences but can refer to a ground-truth bilingual dictionary and large-scale monolingual corpora. |
| Approach: | They propose a task of machine translation that uses a bilingual dictionary and large-scale monolingual corpora to translate a monolingual speaker. |
| Outcome: | The proposed task is based on a bilingual dictionary and large scale monolingual corpora, while being independent on parallel sentences. |
End-to-End Neural Word Alignment Outperforms GIZA++ (2020.acl-main)
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| Challenge: | Word alignment was once a core unsupervised learning task in natural language processing . but word alignment still plays an important role in interactive applications of neural machine translation, such as annotation transfer and lexicon injection. |
| Approach: | They propose to use a Transformer model to train an unsupervised word alignment model. |
| Outcome: | The proposed method outperforms GIZA++ on three data sets and is tightly integrated and does not affect translation quality. |
Generating Synthetic Speech from SpokenVocab for Speech Translation (2023.findings-eacl)
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| Challenge: | End-to-end speech-totext translation (ST) models require large amounts of data to train, but their size is considerably smaller than text-based MT data. |
| Approach: | They propose a method to convert MT data to ST data via text-to-speech systems. |
| Outcome: | The proposed method improves translation quality by an average of 1.83 BLEU score while performing equally well as TTS-generated speech in improving translation quality. |
A Variational Hierarchical Model for Neural Cross-Lingual Summarization (2022.acl-long)
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| Challenge: | Existing studies on cross-lingual summarization focus on pipeline methods or jointly training an end-to-end model through an auxiliary MT or MS objective. |
| Approach: | They propose a hierarchical model for the cross-lingual summarization task . the model is based on the conditional variational auto-encoder . |
| Outcome: | The proposed model generates better cross-lingual summaries than comparison models in the few-shot setting. |
Contextual Refinement of Translations: Large Language Models for Sentence and Document-Level Post-Editing (2024.naacl-long)
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| Challenge: | Large language models have demonstrated considerable success in various natural language processing tasks, but their performance in NMT tasks is still underexplored. |
| Approach: | They propose to use LLMs as automatic post-editors rather than direct translators to improve BLEU and COMET performance. |
| Outcome: | The proposed approach improves BLEU but COMET performance compared to in-context learning. |
BMX: Boosting Natural Language Generation Metrics with Explainability (2024.findings-eacl)
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| Challenge: | Modern language model (LM) based natural language generation evaluation metrics achieve astonishing results in grading machine generated sentences like humans would. |
| Approach: | They use word-level explanations to convert a word- level score into a segment-level score and combine this segment- level with the original metric to obtain a better metric. |
| Outcome: | The proposed metrics improve across MT and summarization datasets while achieving small improvements on machine translation. |
On the Limitations of Cross-lingual Encoders as Exposed by Reference-Free Machine Translation Evaluation (2020.acl-main)
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| Challenge: | a standard evaluation setup for supervised machine learning tasks does not hold for natural language generation tasks. |
| Approach: | They propose to use reference-free machine translation evaluation to compare source texts to system translations to find key limitations. |
| Outcome: | The proposed metrics perform poorly as semantic encoders for reference-free machine translation evaluation. |
A Simple Concatenation can Effectively Improve Speech Translation (2023.acl-short)
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| Challenge: | Experimental results show that in our unified cross-modal ST model, models can effectively utilize the auxiliary information from speech and text. |
| Approach: | They propose a unified cross-modal ST method which concatenates speech and text as the input and builds a teacher that can utilize both cross-modities simultaneously. |
| Outcome: | The proposed method can effectively utilize the auxiliary information from speech and text, and achieve compelling results on MuST-C datasets. |
MMPE: A Multi-Modal Interface for Post-Editing Machine Translation (2020.acl-main)
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Nico Herbig, Tim Düwel, Santanu Pal, Kalliopi Meladaki, Mahsa Monshizadeh, Antonio Krüger, Josef van Genabith
| Challenge: | Current advances in machine translation (MT) increase the need for translators to switch from traditional translation to post-editing (PE) of machine-translated text. |
| Approach: | They propose to combine traditional input modes with pen, touch, and speech modalities for post-editing of machine-translated text. |
| Outcome: | The proposed interfaces are designed to reduce errors and save time. |
Measuring Uncertainty in Translation Quality Evaluation (TQE) (2022.lrec-1)
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| Challenge: | Existing automated tools are not good enough to evaluate translation quality . existing tools are often accused of having low reliability and agreement . |
| Approach: | They propose to use a method to accurately estimate the confidence intervals depending on the sample size of the translated text. |
| Outcome: | The proposed method aims to estimate the confidence intervals (CITATION) depending on the sample size of the translated text, e.g. the amount of words or sentences, that needs to be processed on TQE workflow step for confident and reliable evaluation of overall translation quality. |
On Systematic Style Differences between Unsupervised and Supervised MT and an Application for High-Resource Machine Translation (2022.naacl-main)
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| Challenge: | Modern unsupervised machine translation systems reach reasonable translation quality under clean and controlled data conditions. |
| Approach: | They compare unsupervised and supervised machine translation systems of similar quality . they combine the benefits of both methods into a single system . |
| Outcome: | The proposed system improves adequacy and fluency as measured by human evaluators. |
Evaluating Gender Bias in Machine Translation (P19-1)
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| Challenge: | Using morphological analysis, we find that MT models exhibit gender-biased translation errors when training data encode stereotypes not relevant for the task. |
| Approach: | They propose an automatic gender bias evaluation method for eight target languages with grammatical gender based on morphological analysis. |
| Outcome: | The proposed method is based on two recent coreference resolution datasets composed of English sentences cast participants into non-stereotypical gender roles. |
Grammatical Error Correction through Round-Trip Machine Translation (2023.findings-eacl)
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| Challenge: | A decade ago the idea of using round-trip MT to guide grammatical error correction was not feasible due to the low quality of MT systems of the day. |
| Approach: | They propose to use round-trip machine translation to guide grammatical error correction to preserve meaning while mapping its surface form from one language into another. |
| Outcome: | The proposed system is re-examined across five languages and models of various sizes and yields consistent improvements. |
Japanese Zero Anaphora Resolution Can Benefit from Parallel Texts Through Neural Transfer Learning (2021.findings-emnlp)
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| Challenge: | Using a pretraining model, we find that the performance of Japanese zero anaphora resolution (ZAR) is improved by using machine translation. |
| Approach: | They propose to inject machine translation as an intermediate task between pretraining and ZAR by injecting machine translation into a pretrained BERT model and injecting it into MT. |
| Outcome: | The proposed framework shows that Japanese zero anaphora resolution (ZAR) can be improved by transfer learning from machine translation (MT). |
Hierarchical Modeling of Global Context for Document-Level Neural Machine Translation (D19-1)
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| Challenge: | Document-level machine translation (MT) remains challenging due to the difficulty in efficiently using document context. |
| Approach: | They propose a hierarchical model to learn document context for document-level neural machine translation . they use a sentence encoder to capture intra-sentence dependencies and a document encoder . |
| Outcome: | The proposed model significantly improves document-level translation performance over strong baselines. |
Kreyòl-MT: Building MT for Latin American, Caribbean and Colonial African Creole Languages (2024.naacl-long)
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Nathaniel Robinson, Raj Dabre, Ammon Shurtz, Rasul Dent, Onenamiyi Onesi, Claire Monroc, Loïc Grobol, Hasan Muhammad, Ashi Garg, Naome Etori, Vijay Murari Tiyyala, Olanrewaju Samuel, Matthew Stutzman, Bismarck Odoom, Sanjeev Khudanpur, Stephen Richardson, Kenton Murray
| Challenge: | Creole languages are used in much of Latin America, Africa and the Caribbean . a large multilingual bitext like ours has potential to build the best yet or first ever MT models for many languages . |
| Approach: | They present the largest cumulative dataset to date for Creole language MT . they provide MT models supporting all 41 Creoles in 172 translation directions . |
| Outcome: | The proposed model outperforms a genre-specific Creole MT model on its own benchmark for 23 of 34 translation directions. |
Subword Segmental Machine Translation: Unifying Segmentation and Target Sentence Generation (2023.findings-acl)
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| Challenge: | Subword segmenters are used in neural machine translation, but are not used in high-resource settings. |
| Approach: | They propose a subword segmental machine translation (SSMT) that unifies subword and MT in a single trainable model. |
| Outcome: | The proposed model improves chrF scores for morphologically rich agglutinative languages and is more robust on a test set constructed for evaluating morphology generalisations. |
Mitigating Hallucinated Translations in Large Language Models with Hallucination-focused Preference Optimization (2025.naacl-long)
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| Challenge: | Machine Translation (MT) systems based on fine-tuned large language models (LLMs) are at a higher risk of generating hallucinations, which can severely undermine user’s trust and safety. |
| Approach: | They propose a method that intrinsically learns to mitigate hallucinations during the model training phase. |
| Outcome: | The proposed method reduces hallucinations by 89% on an average across three unseen target languages while preserving translation quality. |
Exploring Cross-Lingual Transfer Learning with Unsupervised Machine Translation (2021.findings-acl)
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| Challenge: | a new CLTL model is proposed to facilitate cross-linguistic transfer learning between distant languages . a key to CLTL is to learn a shared representation space for the given source-target language pair. |
| Approach: | They propose a new CLTL model that integrates machine translation with MT . they use an unannotated data technique to make use of the model's pre-training and fine-tuning . |
| Outcome: | The proposed model achieves better CLTL performance than the baseline model without more annotated data. |
AfriMMT-EA: Multi-domain Machine Translation for Low-Resource East African Languages (2026.findings-eacl)
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Naome A Etori, Kelechi Ezema, Nathaniel Romney Robinson, Davis David, Alfred Malengo Kondoro, Elisha Ondieki Makori, Michael Samwel Mollel, Maria Gini
| Challenge: | Recent advances in open-source large language models have demonstrated strong multilingual capabilities through data-efficient adaptation strategies. |
| Approach: | They propose to use AfriMMT-EA to refine two multilingual versions of Gemma-3 to better understand the region's linguistic and cultural diversity. |
| Outcome: | The proposed datasets comprise 54 local languages across five East African countries. |
JESC: Japanese-English Subtitle Corpus (L18-1)
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| Challenge: | Existing data on Japanese-English subtitles are limited due to the high cost of manual construction. |
| Approach: | They describe the Japanese-English Subtitle Corpus by crawling and aligning subtitles found on the web. |
| Outcome: | The JESC dataset covers the underrepresented domain of conversational dialogue. |
A Survey on Zero Pronoun Translation (2023.acl-long)
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| Challenge: | Zero pronouns (ZPs) are often omitted in pro-drop languages, but should be recalled in non-pro-drop language. |
| Approach: | They propose to analyze the literature on zero pronoun translation after the neural revolution . they uncover that data limitation causes learning bias in languages and domains . |
| Outcome: | The proposed method and methods are compared to other models and evaluation metrics on different benchmarks. |
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. |
Word Alignment as Preference for Machine Translation (2024.emnlp-main)
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| Challenge: | Hallucination and omission are a problem in machine translation because of an LLM's size and low-resource languages. |
| Approach: | They propose to use word alignment as preference to optimize an LLM-based MT model to mitigate hallucination and omission problems. |
| Outcome: | The proposed model is able to mitigate hallucination and omission by using word alignment as preference. |
ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs (2022.findings-naacl)
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| Challenge: | Abstract Meaning Representation (AMR) implicitly involves compound semantic annotations. |
| Approach: | They propose to use auxiliary tasks which are semantically or formally related to enhance AMR parsing. |
| Outcome: | The proposed method achieves state-of-the-art performance on benchmarks especially in topology-related scores. |
Why don’t people use character-level machine translation? (2022.findings-acl)
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| Challenge: | despite evidence character-level systems are comparable with subword systems, they are rarely used in competitive setups in machine translation competitions. |
| Approach: | They propose a two-step decoder architecture that does not suffer from a slow-down due to the length of character sequences. |
| Outcome: | The proposed character-level MT systems show better domain robustness and better morphological generalization . the proposed decoder architecture shows no slow-down due to the length of character sequences . |
Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages (2020.findings-emnlp)
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Wilhelmina Nekoto, Vukosi Marivate, Tshinondiwa Matsila, Timi Fasubaa, Taiwo Fagbohungbe, Solomon Oluwole Akinola, Shamsuddeen Muhammad, Salomon Kabongo Kabenamualu, Salomey Osei, Freshia Sackey, Rubungo Andre Niyongabo, Ricky Macharm, Perez Ogayo, Orevaoghene Ahia, Musie Meressa Berhe, Mofetoluwa Adeyemi, Masabata Mokgesi-Selinga, Lawrence Okegbemi, Laura Martinus, Kolawole Tajudeen, Kevin Degila, Kelechi Ogueji, Kathleen Siminyu, Julia Kreutzer, Jason Webster, Jamiil Toure Ali, Jade Abbott, Iroro Orife, Ignatius Ezeani, Idris Abdulkadir Dangana, Herman Kamper, Hady Elsahar, Goodness Duru, Ghollah Kioko, Murhabazi Espoir, Elan van Biljon, Daniel Whitenack, Christopher Onyefuluchi, Chris Chinenye Emezue, Bonaventure F. P. Dossou, Blessing Sibanda, Blessing Bassey, Ayodele Olabiyi, Arshath Ramkilowan, Alp Öktem, Adewale Akinfaderin, Abdallah Bashir
| Challenge: | 'Low-resourced'-ness is a complex problem that goes beyond data availability and reflects systemic problems in society. |
| Approach: | They propose to use machine translation to scale to low-resourced languages by using a dataset and a benchmarking system to measure their resource use. |
| Outcome: | The proposed approach allows participants without formal training to make a unique scientific contribution. |
To Attend or not to Attend: A Case Study on Syntactic Structures for Semantic Relatedness (P18-1)
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| Challenge: | Recent success of Recurrent Neural Networks (RNNs) in Machine Translation (MT) has prompted attention mechanisms to be used in machine translation. |
| Approach: | They propose a tree-structured attention model on Tree Long Short-Term Memory Networks . they also experiment with three LSTM variants: bidirectional-LSTMs, Constituency Tree-LSTS, and Dependency Tree LSTS. |
| Outcome: | The proposed model is based on tree-LSTMs, constituency trees, and dependencies trees. |
Benchmarking the Performance of Machine Translation Evaluation Metrics with Chinese Multiword Expressions (2024.lrec-main)
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| Challenge: | Multiword Expressions (MWEs) are hard nuts for many natural language processing tasks. |
| Approach: | They annotate 28 types of Chinese MWEs and then examine 31 MTE metrics on groups of sentences containing different MWE. |
| Outcome: | The results show that MT systems and MTE metrics still suffer from MWEs . |
Stacked Acoustic-and-Textual Encoding: Integrating the Pre-trained Models into Speech Translation Encoders (2021.acl-long)
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| Challenge: | End-to-end Speech Translation (E2E ST) encoders lack global context representation, whereas MT encoder lacks it. |
| Approach: | They propose a Stacked Acoustic-and-Textual Encoding method for speech translation . they propose an adaptor module to alleviate representation inconsistency . |
| Outcome: | The proposed method achieves state-of-the-art BLEU scores of 18.3 and 25.2 on two ST tasks. |
Beyond Literal Mapping: Benchmarking and Improving Non-Literal Translation Evaluation (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) have advanced machine translation (MT) a meta-evaluation dataset focused on non-literal translations is lacking . experimental results show the inaccuracies of traditional MT metrics and the limitations of LLM-as-a-Judge. |
| Approach: | They propose a meta-evaluation framework that leverages sub-agents to evaluate machine translation metrics. |
| Outcome: | The proposed framework improves on the knowledge cutoff and score inconsistency problem. |
Direct Segmentation Models for Streaming Speech Translation (2020.emnlp-main)
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Javier Iranzo-Sánchez, Adrià Giménez Pastor, Joan Albert Silvestre-Cerdà, Pau Baquero-Arnal, Jorge Civera Saiz, Alfons Juan
| Challenge: | Existing approaches to stream ST combine advances in ASR and MT to achieve high quality translations without compromising the speed of the system. |
| Approach: | They propose to concatenate an Automatic Speech Recognition system followed by a Machine Translation system. |
| Outcome: | The proposed models improve on the Europarl-ST dataset on the BLEU score. |
Pre-training Multilingual Neural Machine Translation by Leveraging Alignment Information (2020.emnlp-main)
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| Challenge: | Existing pre-training methods are not effective for machine translation tasks. |
| Approach: | They propose a method to pre-train a universal multilingual neural machine translation model . they use random aligned substitution technique to bring words and phrases with similar meanings closer in the representation space. |
| Outcome: | The proposed approach improves translation quality on low, medium, rich resource languages. |
Tilde MT Platform for Developing Client Specific MT Solutions (L18-1)
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| Challenge: | a growing demand for translations and multilingual content is surpassing the supply of professional translation services. |
| Approach: | They present a custom machine translation platform called Tilde MT that provides linguistic data storage, data cleaning and normalisation, statistical and neural machine translation system training and hosting functionality. |
| Outcome: | The proposed platform provides linguistic data storage, data cleaning and normalisation, statistical and neural machine translation system training and hosting functionality, and wide integration capabilities. |
What do Large Language Models Need for Machine Translation Evaluation? (2024.emnlp-main)
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Shenbin Qian, Archchana Sindhujan, Minnie Kabra, Diptesh Kanojia, Constantin Orasan, Tharindu Ranasinghe, Fred Blain
| Challenge: | Existing research shows that large language models can perform better in machine translation tasks. |
| Approach: | They propose to use large language models for machine translation evaluations . authors explore what translation information is needed for LLMs to evaluate MT quality . |
| Outcome: | The proposed model performs comparable to fine-tuned multilingual pre-trained models. |
Phone Features Improve Speech Translation (2020.acl-main)
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| Challenge: | End-to-end models for speech translation more tightly couple speech recognition (ASR) and machine translation (MT) compared to cascades, but performance gap remains in low-resource conditions . |
| Approach: | They propose two methods to incorporate phone features into current neural speech translation models. |
| Outcome: | The proposed models outperform existing models and cascades by up to 9 BLEU on low-resource conditions. |
TEaR: Improving LLM-based Machine Translation with Systematic Self-Refinement (2025.findings-naacl)
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Zhaopeng Feng, Yan Zhang, Hao Li, Bei Wu, Jiayu Liao, Wenqiang Liu, Jun Lang, Yang Feng, Jian Wu, Zuozhu Liu
| Challenge: | Large Language Models (LLMs) have achieved impressive results in Machine Translation (MT). human evaluations reveal that LLM-generated translations still contain various errors. |
| Approach: | They propose a LLM-based self-refinement framework that feeds error information back into LLMs to facilitate self-finement, leading to enhanced translation quality. |
| Outcome: | The proposed framework outperforms internal refinement and feedback methods while ensuring a robust translation quality baseline. |
FCGCL: Fine- and Coarse-Granularity Contrastive Learning for Speech Translation (2022.findings-emnlp)
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| Challenge: | Existing methods to perform implicit knowledge transfer from machine translation to ST model are difficult because of the task complexity and data scarcity. |
| Approach: | They recommend a method which conducts explicit knowledge transfer from MT to ST model by fine and coarse granularity contrastive learning. |
| Outcome: | The proposed method improves the performance of the end-to-end speech translation model on all 8 languages. |
Simple and Effective Retrieve-Edit-Rerank Text Generation (2020.acl-main)
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| Challenge: | Using retrieve-and-edit methods, text generation methods can be improved by reranking outputs from training sets and learning models to produce the final output. |
| Approach: | They propose to extend retrieve-and-edit seq2seq methods with a simple post-generation ranking approach that retrieves multiple outputs and edits each independently to produce the final output. |
| Outcome: | The proposed approach outperforms existing methods on two machine translation datasets and shows room for improvement with better candidate output selection in future work. |
Machine Translation Robustness to Natural Asemantic Variation (2022.emnlp-main)
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| Challenge: | Existing machine translation models struggle with noisy data and tail-end words and phrases. |
| Approach: | They introduce and formalize a class of noise and variation that preserves meaning in the target language. |
| Outcome: | The proposed model can perform better on natural asemantic variation (NAV) the proposed model is robust to a variety of perturbations, but not all of them are achieved with organic variations. |
Effective Self-Mining of In-Context Examples for Unsupervised Machine Translation with LLMs (2025.findings-naacl)
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| Challenge: | Large Language Models (LLMs) have demonstrated impressive performance on a wide range of natural language processing tasks. |
| Approach: | They propose an unsupervised approach to mine in-context examples for machine translation (MT) they use word-level mining to acquire word translations that are then used to perform sentence-level mines . |
| Outcome: | The proposed approach outperforms state-of-the-art methods on 288 directions on 287 languages and is based on word-level mining and sentence-level extraction. |
A Tale of Pronouns: Interpretability Informs Gender Bias Mitigation for Fairer Instruction-Tuned Machine Translation (2023.emnlp-main)
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| Challenge: | Recent instruction fine-tuned models can solve multiple NLP tasks when prompted to do so, but current research focuses on standard performance benchmarks, leaving compelling fairness and ethical considerations behind. |
| Approach: | They propose a method to mitigate gender bias in machine translation by using a corpus of machine translations from the WinoMT corpus. |
| Outcome: | The proposed model can solve multiple NLP tasks when prompted, but it lacks fairness and ethical considerations. |
Segmenting Subtitles for Correcting ASR Segmentation Errors (2021.eacl-main)
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David Wan, Chris Kedzie, Faisal Ladhak, Elsbeth Turcan, Petra Galuscakova, Elena Zotkina, Zhengping Jiang, Peter Bell, Kathleen McKeown
| Challenge: | Typical ASR systems segment input audio into utterances using purely acoustic information, which may not resemble sentence-like units expected by conventional machine translation systems for spoken language translation (SLT). |
| Approach: | They propose a model for correcting ASR acoustic segmentation using subtitles as a proxy dataset for creating synthetic aural utterances by modeling common error modes. |
| Outcome: | The proposed model improves performance on MT and audio-document cross-language information retrieval (CLIR) it uses subtitles as a proxy dataset to correct ASR acoustic segmentation . |
Back Translation for Speech-to-text Translation Without Transcripts (2023.acl-long)
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| Challenge: | End-to-end speech-totext translation (ST) is often achieved by utilizing source transcripts, but transcripts are only sometimes available since numerous unwritten languages exist worldwide. |
| Approach: | They propose an algorithm to synthesize pseudo ST data from monolingual target data to enhance ST without generating source transcripts. |
| Outcome: | The proposed method achieves an average boost of 2.3 BLEU on MuST-C En-De, En-Fr, and En-Es datasets. |
Exploring Segmentation Approaches for Neural Machine Translation of Code-Switched Egyptian Arabic-English Text (2023.eacl-main)
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| Challenge: | Code-switching (CS) is a problem in machine translation, but its performance is not investigated for CS settings. |
| Approach: | They propose to use morphological segmentation techniques for machine translation tasks . they compare morphology-based and frequency-based segmentation for MT tasks based on data size . |
| Outcome: | The proposed approach performs best in MT tasks but under-performs in other languages. |
A Natural Diet: Towards Improving Naturalness of Machine Translation Output (2022.findings-acl)
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| Challenge: | MT evaluation often focuses on accuracy and fluency without paying much attention to translation style. |
| Approach: | They propose a method for training machine translation systems to achieve a more natural style by contrasting training data according to the naturalness of the target side. |
| Outcome: | The proposed method achieves lexical richness on par with human translations, and is preferred by human experts when compared to baseline translations. |
Guiding Large Language Models to Post-Edit Machine Translation with Error Annotations (2024.findings-naacl)
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| Challenge: | supervised systems have not replaced dedicated supervised models for machine translation tasks. |
| Approach: | They propose to guide LLMs to post-edit MT with feedback from MQM annotations . they then fine-tune the LLM to improve its ability to exploit the feedback . |
| Outcome: | The proposed model improves TER, BLEU and COMET scores on Chinese-English, English-German and English-Russian data. |
CDA: a Cost Efficient Content-based Multilingual Web Document Aligner (2021.eacl-main)
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| Challenge: | a Content-based document alignment approach is an efficient way to align multilingual web documents based on content. |
| Approach: | They propose a Content-based document alignment approach to align multilingual web documents based on content in parallel training data for machine translation systems. |
| Outcome: | The proposed method achieves comparable performance with state-of-the-art systems in the WMT-16 Bilingual Document Alignment Shared Task benchmark while operating in multilingual space. |
Self-Supervised Quality Estimation for Machine Translation (2021.emnlp-main)
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Yuanhang Zheng, Zhixing Tan, Meng Zhang, Mieradilijiang Maimaiti, Huanbo Luan, Maosong Sun, Qun Liu, Yang Liu
| Challenge: | Training QE models require massive parallel data with hand-crafted quality annotations, which are time-consuming and labor-intensive to obtain. |
| Approach: | They propose a self-supervised method to evaluate machine-translated sentences without references by recovering masked target words. |
| Outcome: | The proposed method outperforms previous unsupervised methods on several QE tasks in different language pairs and domains. |
Bandits Don’t Follow Rules: Balancing Multi-Facet Machine Translation with Multi-Armed Bandits (2021.findings-emnlp)
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| Challenge: | Training data for machine translation (MT) is often sourced from multiple large corpora that are multi-faceted in nature. |
| Approach: | They propose to optimize the balance between translationese and natural training data to relieve system developers from manual schedule design. |
| Outcome: | The proposed model relieves system developers from manual schedule design. |
DiscoScore: Evaluating Text Generation with BERT and Discourse Coherence (2023.eacl-main)
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| Challenge: | DiscoScore is a parametrized discourse metric that uses BERT to model discourse coherence . it is weak when operated at system level, and is therefore not reliable in a way to spot improvements . |
| Approach: | They propose a parametrized discourse metric which uses BERT to model discourse coherence from different perspectives. |
| Outcome: | The proposed model outperforms existing models on document-level machine translation and summarization. |
Multilingual Machine Translation with Open Large Language Models at Practical Scale: An Empirical Study (2025.naacl-long)
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| Challenge: | Large language models (LLMs) have shown continuously improving multilingual capabilities. |
| Approach: | They evaluate the ability of open LLMs to handle multilingual machine translation tasks using a parallel-first monolingual-second data mixing strategy. |
| Outcome: | The proposed model outperforms state-of-the-art models and achieves competitive performance with Google Translate and GPT-4-turbo. |
AraDiCE: Benchmarks for Dialectal and Cultural Capabilities in LLMs (2025.coling-main)
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Basel Mousi, Nadir Durrani, Fatema Ahmad, Md. Arid Hasan, Maram Hasanain, Tameem Kabbani, Fahim Dalvi, Shammur Absar Chowdhury, Firoj Alam
| Challenge: | a recent study has found that Arabic is underrepresented in Large Language Models, especially in dialectal variations. |
| Approach: | They propose a benchmark for Arabic Dialect and Cultural Evaluation that evaluates Arabic dialect comprehension and generation. |
| Outcome: | The proposed model outperforms multilingual models on dialect comprehension and generation, but significant challenges persist in dialect identification, generation, and translation. |
Cross-lingual Cross-modal Pretraining for Multimodal Retrieval (2021.naacl-main)
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| Challenge: | Recent pretrained vision-language models have achieved impressive performance on cross-modal retrieval tasks in English. |
| Approach: | They propose a new approach to learn cross-lingual cross-modal representations for matching images and captions in multiple languages using an annotated corpus. |
| Outcome: | The proposed model achieves impressive performance on two multimodal multilingual image caption benchmarks: Multi30k with German captions and MSCOCO with Japanese captions. |
A Simple and Effective Approach to Automatic Post-Editing with Transfer Learning (P19-1)
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| Challenge: | Existing APE systems generate artificial triplets of source sentences, machine translation outputs and human post-edits. |
| Approach: | They propose to use human post-edits to refine black-box machine translation (MT) models by fine-tuning pre-trained BERT models on both encoder and decoder of an APE system. |
| Outcome: | The proposed method improves on a dataset of 23K sentences on x86 GPUs. |
Beyond Reference: Evaluating High Quality Translations Better than Human References (2024.emnlp-main)
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| Challenge: | Existing machine translation metrics give maximum score to reference sentence . however, these metrics overlook the possibility that candidate sentences outperform reference sentences in terms of quality. |
| Approach: | They propose a machine translation metrics that give an absolute score to a translated sentence based on the similarity with the reference sentence. |
| Outcome: | The proposed measure outperforms existing MT metrics in terms of quality and assigns positive scores to candidates that outperformed reference sentences. |
To Translate or Not to Translate: A Systematic Investigation of Translation-Based Cross-Lingual Transfer to Low-Resource Languages (2024.naacl-long)
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| Challenge: | XLT with multilingual language models is superfluous, says a new study . mBERT, XLM-R and mT5 are effective for cross-lingual transfer, authors say . |
| Approach: | They propose to use multilingual language models to improve cross-lingual transfer (XLT) they propose to add reliable translations to training data for XLT even for non-MT languages . |
| Outcome: | The proposed approaches outperform zero-shot XLT with mLMs, the authors show . the authors believe their findings warrant a broader inclusion of more robust translation-based baselines in XL research. |
Recipes for Adapting Pre-trained Monolingual and Multilingual Models to Machine Translation (2021.eacl-main)
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| Challenge: | Recent advances in machine translation (MT) have improved performance on low-resource language pairs. |
| Approach: | They propose to freeze most BART parameters and add new ones to fine-tune a model trained on MT. |
| Outcome: | The proposed model outperforms naive fine-tuning on Vietnamese to English on a training set for Vietnamese to Vietnamese . the proposed model is able to fine- tune on smaller datasets while still maintaining the same model performance. |
Clarify When Necessary: Resolving Ambiguity Through Interaction with LMs (2025.findings-naacl)
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| Challenge: | Ambiguity is embedded throughout natural language, and even simple utterances can have multiple interpretations when read in isolation. |
| Approach: | They propose a task-agnostic framework for evaluating a system’s ability to determine when to ask for clarification. |
| Outcome: | The proposed framework outperforms existing uncertainty estimation approaches at identifying predictions that will benefit from clarification. |
DOLFIN - Document-Level Financial Test-Set for Machine Translation (2025.findings-naacl)
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| Challenge: | Existing document-level machine translation test-sets cover general domain but fall short on specialised domains, such as legal and financial. |
| Approach: | They propose to use a document-level machine translation test-set to replace perfectly aligned sentences by presenting data in units of sections rather than sentences. |
| Outcome: | The proposed dataset is built from specialised financial documents and it shows that it can discriminate between context-sensitive and context-agnostic models and shows the weaknesses when models fail to accurately translate financial texts. |
Rethinking Coherence Modeling: Synthetic vs. Downstream Tasks (2021.eacl-main)
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| Challenge: | Coherence models are typically evaluated only on synthetic tasks, which may not be representative of their performance in downstream applications. |
| Approach: | They compare models' performance on synthetic sentences with those on retrieval-based dialog. |
| Outcome: | The proposed models perform poorly on synthetic sentences and retrieval-based dialog tasks. |
Gated Transformer for Robust De-noised Sequence-to-Sequence Modelling (2021.findings-emnlp)
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| Challenge: | Noisy texts are common in user-generated texts that appear abundant in social media platforms like SMS, online chat, email, blogs, wikis etc. |
| Approach: | They propose a sequence-to-sequence architecture that uses a gating mechanism to detect types of corrections required from English texts. |
| Outcome: | The proposed architecture performs better than non-gated models on machine translation and Summarization tasks. |
Retrieval-Augmented Machine Translation with Unstructured Knowledge (2025.findings-emnlp)
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| Challenge: | Retrieval-augmented generation (RAG) is a new approach to enhance large language models (LLMs). |
| Approach: | They propose a multi-task training method to teach LLMs how to use information from multilingual documents during their translation. |
| Outcome: | The proposed method improves LLMs by 1.6-3.1 BLEU and 1.0-2.0 COMET scores in En-Zh, and 1.7-2.9 BLUE and 2.1-2.7 COMET score in En de. |
On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models (N19-1)
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| Challenge: | Existing methods for assessing the robustness of sequence-to-sequence models have been ignored by the literature. |
| Approach: | They propose an evaluation framework for adversarial attacks on seq2seq models that takes the semantic equivalence of the pre- and post-perturbation input into account. |
| Outcome: | The proposed framework breaks the assumption that source perturbations should not result in changes in the expected output, but allows for meaning-preserving perturbations that change the output sequence. |
Adaptation of Back-translation to Automatic Post-Editing for Synthetic Data Generation (2021.eacl-main)
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| Challenge: | Automated Post-Editing (APE) aims to correct errors in the output of a given machine translation system. |
| Approach: | They propose two new methods of synthesizing additional MT outputs by adapting back-translation to the APE task, obtaining robust enlargements of existing synthetic APE training dataset. |
| Outcome: | The proposed methods improve translation quality on the English-German APE task by enlarging the existing training dataset. |
Mutual-Learning Improves End-to-End Speech Translation (2021.emnlp-main)
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| Challenge: | Existing approaches to end-to-end speech translation (E2E) models only allow one way knowledge transfer, which is limited by the performance of the teacher model. |
| Approach: | They propose a one-way knowledge transfer paradigm where the MT and ST models are collaboratively trained and considered as peers rather than teacher/student. |
| Outcome: | The proposed model improves the performance of end-to-end speech translation (ST) task by combining knowledge from two models with peer models. |
Benchmarking Neural and Statistical Machine Translation on Low-Resource African Languages (2020.lrec-1)
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| Challenge: | a recent study has focused on languages where large amounts of resources are available. |
| Approach: | They benchmark state of the art statistical and neural machine translation systems on Somali and Swahili languages . they find that statistical machine translation and neural translation can perform similarly in low-resource scenarios . |
| Outcome: | The results show that statistical machine translation and neural machine translation perform similarly in low-resource scenarios. |
Large Language Model for Multi-Domain Translation: Benchmarking and Domain CoT Fine-tuning (2024.findings-emnlp)
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| Challenge: | Achieving consistent high-quality machine translation across diverse domains remains a challenge due to limited and imbalanced parallel training data available in various domains. |
| Approach: | They propose a domain Chain of Thought technique that uses the multi-domain intelligence of LLMs to improve translation performance. |
| Outcome: | The proposed method achieves significant improvements in translation accuracy and domain robustness over traditional fine-tuning on a small dataset of four domains. |
Evaluating Machine Translation Datasets for Low-Web Data Languages: A Gendered Lens (2026.findings-acl)
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Hellina Hailu Nigatu, Bethelhem Yemane Mamo, Bontu Fufa Balcha, Debora Taye Tesfaye, Elbethel Daniel Zewdie, Ikram Behiru Nesiru, Jitu Ewnetu Hailu, Senait Mengesha Yayo
| Challenge: | afan oromo, amharic, and tigrinya are low-resourced languages . they are used for training, benchmarks, news, health, and sports . afono o'mara: quantity does not guarantee quality of MT datasets . |
| Approach: | They investigate the quality of machine translation datasets for three low-resourced languages . they found a large skew towards the male gender in the datasets . |
| Outcome: | The results show that training data has large representation of political and religious text, but benchmark datasets focus on news, health, and sports. |
SrcMix: Mixing of Related Source Languages Benefits Extremely Low-resource Machine Translation (2026.findings-eacl)
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| Challenge: | Multilingual models are widely used for machine translation, but their effectiveness for extremely low-resource languages (ELRLs) is dependent on how related languages are incorporated during fine-tuning. |
| Approach: | They propose a source-side mixing strategy that combines related ELRLs during fine-tuning while constraining the decoder to a single target language. |
| Outcome: | The proposed approach improves performance in high-resource to ELRL translations and in mid-resourced to MT translations. |
Transforming Complex Sentences into a Semantic Hierarchy (P19-1)
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| Challenge: | Existing approaches for recursively splitting and rephrasing complex English sentences into a semantic hierarchy of simplified sentences are lacking. |
| Approach: | They propose a method for recursively splitting and rephrasing complex English sentences into a semantic hierarchy of simplified sentences. |
| Outcome: | The proposed approach outperforms state-of-the-art approaches in MT and information extraction tasks. |
TasTe: Teaching Large Language Models to Translate through Self-Reflection (2024.acl-long)
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| Challenge: | Existing approaches to enhance LLMs' performance in machine translation are unable to fully exploit their instruction-following capabilities. |
| Approach: | They propose a framework for translating through self-reflection that involves two stages of inference . they propose to use the framework to refine LLMs' preliminary translations . |
| Outcome: | The proposed framework can produce translation outputs that match the quality of NMT systems. |
SURF: Semantic-level Unsupervised Reward Function for Machine Translation (2022.naacl-main)
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| Challenge: | Reinforcement Learning (RL) is dependent on the reward formulation due to the intrinsic difficulty of the task in the high-dimensional discrete action space and the sparseness of the standard reward functions. |
| Approach: | They propose a maximally dense semantic-level unsupervised reward function which mimics human evaluation by considering both sentence fluency and semantic similarity. |
| Outcome: | The proposed reward outperforms the standard sparse reward by 2% on average for in- and out-of-domain settings. |
AfriMTE and AfriCOMET: Enhancing COMET to Embrace Under-resourced African Languages (2024.naacl-long)
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Jiayi Wang, David Adelani, Sweta Agrawal, Marek Masiak, Ricardo Rei, Eleftheria Briakou, Marine Carpuat, Xuanli He, Sofia Bourhim, Andiswa Bukula, Muhidin Mohamed, Temitayo Olatoye, Tosin Adewumi, Hamam Mokayed, Christine Mwase, Wangui Kimotho, Foutse Yuehgoh, Anuoluwapo Aremu, Jessica Ojo, Shamsuddeen Muhammad, Salomey Osei, Abdul-Hakeem Omotayo, Chiamaka Chukwuneke, Perez Ogayo, Oumaima Hourrane, Salma El Anigri, Lolwethu Ndolela, Thabiso Mangwana, Shafie Mohamed, Hassan Ayinde, Oluwabusayo Awoyomi, Lama Alkhaled, Sana Al-azzawi, Naome Etori, Millicent Ochieng, Clemencia Siro, Njoroge Kiragu, Eric Muchiri, Wangari Kimotho, Toadoum Sari Sakayo, Lyse Naomi Wamba, Daud Abolade, Simbiat Ajao, Iyanuoluwa Shode, Ricky Macharm, Ruqayya Iro, Saheed Abdullahi, Stephen Moore, Bernard Opoku, Zainab Akinjobi, Abeeb Afolabi, Nnaemeka Obiefuna, Onyekachi Ogbu, Sam Ochieng’, Verrah Otiende, Chinedu Mbonu, Yao Lu, Pontus Stenetorp
| Challenge: | Recent advances in machine translation (MT) have focused on scaling multilingual machine translation models and evaluation data to hundreds of languages, including multiple under-resourced languages. |
| Approach: | They propose to use n-gram matching metrics to measure progress in multilingual machine translation to 13 typologically diverse African languages to create high-quality human evaluation data with simplified MQM guidelines. |
| Outcome: | The proposed metrics have a higher correlation with human judgments than n-gram matching metrics such as BLEU and METEOR. |
ParaZh-22M: A Large-Scale Chinese Parabank via Machine Translation (2022.coling-1)
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| Challenge: | Paraphrasing is an important data augmentation approach for natural language processing (NLP). |
| Approach: | They propose to extract sentence-level paraphrases from multiple Chinese translations and construct a larger Chinese parabank with 22M sentence pairs. |
| Outcome: | The proposed parabank is the largest to date in Chinese, but limited by one-to-many translation data. |
VietMix: A Naturally-Occurring Parallel Corpus and Augmentation Framework for Vietnamese-English Code-Mixed Machine Translation (2026.eacl-long)
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| Challenge: | Existing approaches to machine translation (MT) systems degrade when faced with code-mixed text. |
| Approach: | They propose a system that can augment Vietnamese-English code-mixed text with iterative fine-tuning and targeted filtering. |
| Outcome: | The proposed framework outperforms strong back-translation baselines and improves zero-shot models by up to +11.9 points. |
Measuring Robustness for NLP (2022.coling-1)
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| Challenge: | Existing methods to evaluate NLP models are limited to news domains and cannot be generalized to other domains. |
| Approach: | They propose a measure of NLP quality based on robustness . they measure consistency of cross-domain accuracy and introduce coefficient of variation and gamma-Robustness based upon human evaluation . |
| Outcome: | The proposed approach shows higher agreement with human evaluation than accuracy scores on ranking machine translation systems. |
It’s not a Non-Issue: Negation as a Source of Error in Machine Translation (2020.findings-emnlp)
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| Challenge: | In this study, we focus on negation, a universal, core property of human language that affects the semantics of an utterance. |
| Approach: | They focus on negation, a universal, core property of human language that affects semantics of an utterance. |
| Outcome: | The proposed method improves translation quality by 60% in some cases . the authors also provide a linguistically motivated analysis that directly explains the majority of the results. |
On Creating an English-Thai Code-switched Machine Translation in Medical Domain (2024.findings-emnlp)
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Parinthapat Pengpun, Krittamate Tiankanon, Amrest Chinkamol, Jiramet Kinchagawat, Pitchaya Chairuengjitjaras, Pasit Supholkhan, Pubordee Aussavavirojekul, Chiraphat Boonnag, Kanyakorn Veerakanjana, Hirunkul Phimsiri, Boonthicha Sae-jia, Nattawach Sataudom, Piyalitt Ittichaiwong, Peerat Limkonchotiwat
| Challenge: | despite advances in English-Thai MT, common MT approaches often underperform in the medical field due to their inability to precisely translate medical terminologies. |
| Approach: | They propose to maintain medical terminology in English within translated text through code-switched translation. |
| Outcome: | The proposed method shows that medical professionals prefer CS translations that maintain critical English terms accurately, even if it slightly compromises fluency. |
WeTS: A Benchmark for Translation Suggestion (2022.emnlp-main)
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| Challenge: | Existing studies focus on overall performance of machine translation but ignore TS performance, authors say . if TS is applied into post-editing, it will reduce the time and cost of post-production. |
| Approach: | They propose to use a golden corpus annotated by experts to generate a translation suggestion model. |
| Outcome: | The proposed model improves on the golden corpus annotated by translators on four translation directions. |
The Challenges of Optimizing Machine Translation for Low Resource Cross-Language Information Retrieval (D19-1)
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| Challenge: | Existing studies do not investigate the effectiveness of MT metrics in predicting performance of downstream IR models. |
| Approach: | They examine the relationship between MT performance and IR quality in a CLIR-based system . they find that the choice of IR collection can significantly affect MT tuning decisions . |
| Outcome: | The proposed model can predict CLIR performance better from MT quality, the authors show . the proposed model is based on a BLEU-based model with a bag of words constraint . |
Grammar-based Data Augmentation for Low-Resource Languages: The Case of Guarani-Spanish Neural Machine Translation (2024.naacl-long)
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Agustín Lucas, Alexis Baladón, Victoria Pardiñas, Marvin Agüero-Torales, Santiago Góngora, Luis Chiruzzo
| Challenge: | Low-resource languages suffer from a vicious circle: data is needed to build tools, but available text is scarce. |
| Approach: | They propose to use a grammar-based system to generate Spanish text and syntactically transfer it to Guarani to boost its performance. |
| Outcome: | The proposed system outperforms existing models by pretraining models with synthetic text. |
Discrete Cross-Modal Alignment Enables Zero-Shot Speech Translation (2022.emnlp-main)
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| Challenge: | Existing zero-shot methods fail to align speech and text into a shared semantic space . Existing methods require expensive and expensive parallel ST data . |
| Approach: | They propose a method that uses a shared discrete vocabulary space to align speech and text into a common space. |
| Outcome: | The proposed method significantly improves the SOTA and even performs on par with the strong supervised ST baselines. |
A Deep Analysis of the Impact of Multiword Expressions and Named Entities on Chinese-English Machine Translations (2024.findings-emnlp)
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| Challenge: | a study on the impact of multiword expressions and multiword named entities (NEs) on the performance of Chinese-English machine translation systems is presented. |
| Approach: | They propose to use Chinese multiword expressions and multiword named entities (NEs) to evaluate machine translation performance. |
| Outcome: | The proposed methods show that Chinese-English machine translation systems perform significantly worse on Chinese sentences with most kinds of MWEs and NEs. |
Selecting Backtranslated Data from Multiple Sources for Improved Neural Machine Translation (2020.acl-main)
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| Challenge: | incorporating backtranslated data from different sources has led to improved results in machine translation (MT) |
| Approach: | They use a low-resource use-case and a high-resourced language pair to test different backtranslation scenarios and employ data selection to optimise the synthetic corpora. |
| Outcome: | The proposed method reduces the amount of data used while maintaining high-quality MT systems. |
Improving Machine Translation with Phrase Pair Injection and Corpus Filtering (2022.emnlp-main)
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| Challenge: | In this paper, we show that the combination of Phrase Pair Injection and Corpus Filtering boosts the performance of Neural Machine Translation systems. |
| Approach: | They propose to combine Phrase Pair Injection and Corpus Filtering to boost performance of Neural Machine Translation systems. |
| Outcome: | The proposed method improves machine translation models on low-resource language pairs . BLEU score improves over models trained with whole pseudo-parallel corpus augmented with parallel corpus. |
MultiMWE: Building a Multi-lingual Multi-Word Expression (MWE) Parallel Corpora (2020.lrec-1)
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| Challenge: | Existing bilingual or multi-lingual MWE corpora are limited for multilingual use . only 871 pairs of English-German MWEs are available for research . |
| Approach: | They present a collection of bilingual and multi-lingual MWEs extracted from parallel corpora. |
| Outcome: | The available bilingual or multi-lingual MWE corpus is very limited . the collection is a small collection of 871 pairs of English-German MWEs . |
OTTAWA: Optimal TransporT Adaptive Word Aligner for Hallucination and Omission Translation Errors Detection (2024.findings-acl)
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| Challenge: | Existing methods for detecting hallucinations and omissions in Machine Translation systems focus on analyzing the model’s internal states or relying on external tools. |
| Approach: | They propose an Optimal Transport-based word aligner specifically designed to enhance the detection of hallucinations and omissions in Machine Translation systems. |
| Outcome: | The proposed method is competitive with state-of-the-art methods across 18 language pairs on the HalOmi benchmark and shows promising features. |
Max-Margin Incremental CCG Parsing (2020.acl-main)
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| Challenge: | a new incremental parser reduces the number of beam search violations and minimises the biggest violation. |
| Approach: | They propose to use beam search optimisation to minimise all beam search violations instead of minimising only the biggest violation. |
| Outcome: | The proposed parser outperforms existing non-incremental parsers and minimises all beam search violations instead of minimising the biggest violation. |
SALTED: A Framework for SAlient Long-tail Translation Error Detection (2022.findings-emnlp)
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| Challenge: | Traditional machine translation metrics are insensitive to the long tail of behavioral problems. |
| Approach: | They propose a specification-based framework for behavioral testing of NMT models . they use high-precision detectors that flag errors between a source sentence and output . |
| Outcome: | The proposed framework provides a reliable view of problems that were previously invisible. |
The Two Shades of Dubbing in Neural Machine Translation (2020.coling-main)
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| Challenge: | Dubbing has two shades; synchronisation constraints are applied only when the actor’s mouth is visible on screen, while the translation is unconstrained for off-screen dubbing. |
| Approach: | They annotate an existing dubbing corpus for this dichotomy and find that on-screen dubbing is more difficult for MT than off-screen. |
| Outcome: | The results show that on-screen dubbing is more difficult for MT than off-screen translation, and that synchronisation constraints dramatically decrease translation quality for off- screen dubbing. |
Lost in Back-Translation: Emotion Preservation in Neural Machine Translation (2020.coling-main)
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| Challenge: | MT is used to support human-to-human communication across languages, but it is unclear whether it can translate the non-propositional level of emotions. |
| Approach: | They propose to use a re-ranking approach to change emotions to reverse this tendency . they find that emotions are toned down or amplified through linguistic changes . |
| Outcome: | The proposed model can be used to change emotions, and it can be applied to other languages. |
Intermediate Self-supervised Learning for Machine Translation Quality Estimation (2020.coling-main)
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| Challenge: | Existing methods for machine translation quality estimation (QE) rely on annotated data. |
| Approach: | They propose a self-supervised learning task for machine translation (MT) that orients a pre-trained model towards the target task. |
| Outcome: | The proposed method outperforms existing methods on English-to-German and English- to-Russian translation directions and is comparable to existing models. |
Self-Distillation for Model Stacking Unlocks Cross-Lingual NLU in 200+ Languages (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) excel on English NLU tasks, yet struggle to extend their NLU capabilities to underrepresented languages. |
| Approach: | They integrate machine translation models (MT) directly into LLM backbones via sample-efficient self-distillation. |
| Outcome: | The proposed model outperforms translation-test models on 127 low-resource languages. |
Where’s the Point? Self-Supervised Multilingual Punctuation-Agnostic Sentence Segmentation (2023.acl-long)
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| Challenge: | Prior sentence segmentation tools rely on punctuation or require a large amount of training data . a new method for multilingual sentence segmenting is proposed to replace the best prior tools by using only sentence-segmented examples. |
| Approach: | They propose a punctuation-agnostic sentence segmentation method that uses newline characters which implicitly perform segmentation into paragraphs. |
| Outcome: | The proposed method outperforms all prior best sentence segmentation tools by 6.1% F1 points. |
Revisiting Machine Translation for Cross-lingual Classification (2023.emnlp-main)
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| 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. |
Reinforcement Learning with Large Action Spaces for Neural Machine Translation (2022.coling-1)
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| Challenge: | Recent work has argued that the gains produced by Reinforcement learning are mostly due to promoting tokens that have already received a fairly high probability in pre-training. |
| Approach: | They hypothesize that the large action space is a main obstacle to RL’s effectiveness in MT by reducing the size of the vocabulary without changing the vocabulary. |
| Outcome: | The proposed method improves by 1.5 BLEU points on average. |
Keeping Notes: Conditional Natural Language Generation with a Scratchpad Encoder (P19-1)
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| Challenge: | Qualitative assessments in the form of human judgements (question generation), attention visualization (MT), and sample output (summarization) provide further evidence of the ability of Scratchpad to generate fluent and expressive output. |
| Approach: | They propose to use the encoder as a "scratchpad" memory to keep track of what has been generated and guide future generation. |
| Outcome: | The proposed mechanism improves the fluency of seq2seq models on three well-studied natural language generation tasks. |
Data Cartography for Low-Resource Neural Machine Translation (2022.findings-emnlp)
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| Challenge: | Existing methods to improve machine translation (MT) in low-resource settings are limited in the number of languages spoken in the world. |
| Approach: | They apply cartography techniques to characterize the contribution of training samples in two low-resource MT tasks (Swahili-English and Turkish-English) they argue that data augmentation strategies for low-Resource ML would benefit from model-in-the-loop strategies to maximize improvements. |
| Outcome: | The proposed methods show that training samples contribute to model training in low-resource MT tasks, albeit not uniformly throughout the training process. |
Interplay of Machine Translation, Diacritics, and Diacritization (2024.naacl-long)
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| Challenge: | MT and diacritization influence performance in a multi-task learning setting, but keeping diacritics is harmful for some languages. |
| Approach: | They propose two classes of metrics to measure the complexity of a diacritical system and propose to use them to compare performance. |
| Outcome: | The proposed metrics correlate positively with the performance of the models. |
Detecting Critical Errors Considering Cross-Cultural Factors in English-Korean Translation (2024.lrec-main)
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Sugyeong Eo, Jungwoo Lim, Chanjun Park, DaHyun Jung, Seonmin Koo, Hyeonseok Moon, Jaehyung Seo, Heuiseok Lim
| Challenge: | Recent machine translation systems overcome language barriers for a wide range of users, yet they carry the risk of catastrophic meaning deviations. |
| Approach: | They introduce a culture-aware "Politeness" type for detecting critical translation errors . they also provide multiclass labels for critical error detection and critical error type classification . |
| Outcome: | Empirical results show that the proposed method outperforms baselines in both tasks. |
FooTweets: A Bilingual Parallel Corpus of World Cup Tweets (L18-1)
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| Challenge: | a new study analyzes the nature of twitter data and compares it with other social networking websites. |
| Approach: | They develop a parallel corpus of tweets for an English-German pair that can be translated into German using a machine translation tool. |
| Outcome: | The proposed method can be used to translate tweets from English to German using a parallel corpus of 4, 000 tweets. |
Benchmarking and Improving Long-Text Translation with Large Language Models (2024.findings-acl)
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Longyue Wang, Zefeng Du, Wenxiang Jiao, Chenyang Lyu, Jianhui Pang, Leyang Cui, Kaiqiang Song, Derek Wong, Shuming Shi, Zhaopeng Tu
| Challenge: | Recent studies have illuminated the promising capabilities of large language models (LLMs) in handling long texts. |
| Approach: | They construct a benchmark dataset specifically designed for the finetuning and evaluation of large language models (LLMs) they compare LLMs with MT models and find they exhibit shortcomings in long-text domains . |
| Outcome: | The proposed model performs better in long-text translation, and its performance diminishes as document size increases. |
Understanding In-Context Machine Translation for Low-Resource Languages: A Case Study on Manchu (2025.acl-long)
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| Challenge: | In-context machine translation (MT) with large language models can take advantage of linguistic resources such as grammar books and dictionaries. |
| Approach: | They propose to use in-context machine translation (MT) with large language models to take advantage of linguistic resources such as grammar books and dictionaries. |
| Outcome: | The proposed approach can take advantage of dictionaries and grammar books, but its performance is poor for many lowresource languages. |
Discourse-Centric Evaluation of Document-level Machine Translation with a New Densely Annotated Parallel Corpus of Novels (2023.acl-long)
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| Challenge: | Several recent papers claim to have achieved human parity at sentence-level machine translation. |
| Approach: | They propose to use a dataset with rich discourse annotations to evaluate MT performance . they find that MT outputs differ fundamentally from human translations in terms of latent discourse structures. |
| Outcome: | The proposed dataset builds upon the large-scale parallel corpus BWB . it covers 15,095 entity mentions in both languages and compares them to human translations . |
An Empirical Study of In-context Learning in LLMs for Machine Translation (2024.findings-acl)
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| Challenge: | Recent studies focus on optimizing translation quality, with limited attention to understanding specific aspects of ICL that influence the said quality. |
| Approach: | They conduct the first of its kind, exhaustive study of in-context learning for machine translation (MT) they establish that ICL is primarily example-driven and not instruction-driven . |
| Outcome: | The proposed model is based on examples and not instruction-driven learning. |
Informative Manual Evaluation of Machine Translation Output (2020.coling-main)
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| 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. |
Imitation Attacks and Defenses for Black-box Machine Translation Systems (2020.emnlp-main)
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| Challenge: | Using simulated experiments, we demonstrate that MT systems can be stolen even when imitation models have different input data or architectures than their target models. |
| Approach: | They propose a defense that modifies translation outputs to misdirect optimization of imitation models. |
| Outcome: | The proposed defense degrades the adversary’s BLEU score and attack success rate at some cost in the defender’s performance and inference speed. |
NMT and PBSMT Error Analyses in English to Brazilian Portuguese Automatic Translations (2020.lrec-1)
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| Challenge: | Recent work proposes neural machine translation (NMT) for Brazilian Portuguese. |
| Approach: | They propose a neural machine translation approach that generates equivalent sentences in target language and source language. |
| Outcome: | The proposed approach outperforms phrase-based statistical machine translation systems for some pairs of languages. |
DUB: Discrete Unit Back-translation for Speech Translation (2023.findings-acl)
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| Challenge: | Discrete unit back-translation (DUB) is a back-translated speech-to-text translation (ST) technique that can be applied to ST . a modality gap between speech and text makes it difficult to transfer these techniques to ST due to the modality of the speech-text model. |
| Approach: | They propose a method to represent speech with discrete units instead of continuous features in direct ST. |
| Outcome: | The proposed method achieves comparable performance to existing methods that rely on large-scale external data. |
Building Bridges: A Dataset for Evaluating Gender-Fair Machine Translation into German (2024.findings-acl)
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| Challenge: | a societal movement towards using gender-fair language exists, but gender-free German is barely supported in machine translation. |
| Approach: | They propose to use a community-created gender-fair language dictionary to study gender-neutral German . they also use encyclopedic text and parliamentary speeches to translate the words in isolation . |
| Outcome: | The proposed study shows that most systems produce mainly masculine forms and rarely gender-neutral variants. |
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. |
Jointly Learning to Align and Translate with Transformer Models (D19-1)
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| Challenge: | Existing word alignment models are not accurate for word alignments. |
| Approach: | They propose a method to train a Transformer model to produce accurate translations and alignments. |
| Outcome: | The proposed model outperforms GIZA++ trained models on translation and alignment tasks while maintaining translation accuracy. |
On Context Span Needed for Machine Translation Evaluation (2020.lrec-1)
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| Challenge: | a number of common patterns can be observed for context-aware MT evaluation, authors say . document-level evaluations have largely been performed at the sentence level . the definition of what constitutes a "document level" evaluation is still unclear . |
| Approach: | They propose to use a series of surveys to identify the necessary context span . they find common patterns that can be used to draw general guidelines . |
| Outcome: | The proposed evaluations of machine translation systems show that some issues and spans depend on domain and target language. |
Low-Rank Softmax Can Have Unargmaxable Classes in Theory but Rarely in Practice (2022.acl-long)
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| Challenge: | Probabilistic multiclass classifiers with large number of output classes are commonplace in natural language processing. |
| Approach: | They propose to use argmax to predict words from a large vocabulary in NLP models . they find that 13 out of 150 models do indeed have such unargmaxable tokens . |
| Outcome: | The proposed algorithms detect unargmaxable tokens in large language models and translation models. |
M2PO: Multi-Perspective Multi-Pair Preference Optimization for Machine Translation (2026.acl-long)
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Hao Wang, Linlong Xu, Heng Liu, Yangyang Liu, Xiaohu Zhao, Bo Zeng, Liangying Shao, Yichen Dong, Xinwei Wu, Jiang Zhou, Tianyu Dong, Xiangxiang Zeng, Longyue Wang, Weihua Luo
| Challenge: | prevailing methods for machine translation are often hindered by misleading reward signals. |
| Approach: | They propose a framework that aligns large language models to human preferences . they propose 'M2PO' to correct the bias towards partial errors . |
| Outcome: | The proposed framework outperforms open-source models and achieves parity with proprietary models. |
A Large-Scale Study of Machine Translation in Turkic Languages (2021.emnlp-main)
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Jamshidbek Mirzakhalov, Anoop Babu, Duygu Ataman, Sherzod Kariev, Francis Tyers, Otabek Abduraufov, Mammad Hajili, Sardana Ivanova, Abror Khaytbaev, Antonio Laverghetta Jr., Bekhzodbek Moydinboyev, Esra Onal, Shaxnoza Pulatova, Ahsan Wahab, Orhan Firat, Sriram Chellappan
| Challenge: | a large corpus covering 22 Turkic languages is included in this paper . low-resource MT evaluation has traditionally focused on European languages due to limitations of available technology and resources. |
| Approach: | They present a case study of the practical application of MT in the Turkic language family . they propose to realize the gains of NMT for Turkic languages under high-resource to extremely low-resourced scenarios. |
| Outcome: | The proposed study shows that the new methods can be used in the Turkic language family . the results highlight bottlenecks in building competitive systems . |
Rule-based Morphological Inflection Improves Neural Terminology Translation (2021.emnlp-main)
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| Challenge: | Current approaches to incorporating terminology constraints in machine translation (MT) typically assume that the constraint terms are provided in their correct morphological forms. |
| Approach: | They propose a framework for incorporating lemma constraints in machine translation . they use a cross-lingual inflection module that inflects the target lemmo constraints based on the source context. |
| Outcome: | The proposed framework outperforms existing methods with lower training costs and linguistic knowledge in domain adaptation and low-resource MT settings. |
Leveraging Large Pre-trained Multilingual Models for High-Quality Speech-to-Text Translation on Industry Scenarios (2025.coling-main)
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| Challenge: | Speech-to-Text Translation systems rely on a sequential pipeline that combines ASR and MT models. |
| Approach: | They propose a parameter-efficient framework that integrates one LPSM with a multilingual MT engine. |
| Outcome: | The proposed framework integrates one LPSM with a multilingual MT engine. |
Cross-lingual neural fuzzy matching for exploiting target-language monolingual corpora in computer-aided translation (2022.emnlp-main)
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| Challenge: | CAT tools based on translation memories (TMs) are limited in their use for a number of translation tasks due to the limited availability of in-domain TMs. |
| Approach: | They propose a neural approach to exploit in-domain TMs and in-target-language (TL) monolingual corpora to exploit CAT tools. |
| Outcome: | The proposed approach exploits in-domain TMs and in-target-language (TL) monolingual corpora and increases translation proposals on four language pairs. |
PheMT: A Phenomenon-wise Dataset for Machine Translation Robustness on User-Generated Contents (2020.coling-main)
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| Challenge: | Existing studies suggest that Neural Machine Translation still struggles with certain kinds of input with considerable noise, such as User-Generated Contents (UGC) on the Internet. |
| Approach: | They propose to evaluate the robustness of Neural Machine Translation models against specific linguistic phenomena in Japanese-English translation. |
| Outcome: | The proposed model can handle user-generated content (UGC) on the Internet, but it is difficult to translate clean inputs. |
Mid-Air Hand Gestures for Post-Editing of Machine Translation (2021.acl-long)
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| Challenge: | In a well-connected world, translation is of everincreasing importance. |
| Approach: | They propose to use mid-air hand gestures in combination with the keyboard for editing in machine translation and post-editing workflows to improve quality. |
| Outcome: | The proposed prototype supports mid-air hand gestures for cursor placement, text selection, deletion, and reordering. |
The statistical advantage of automatic NLG metrics at the system level (2021.acl-long)
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| Challenge: | Statistically, humans are unbiased, high variance estimators, while metrics are biased, low variance estimator. |
| Approach: | They compare automatic metrics to humans and a derived, perfect segment-level annotator by applying a bias-variance-noise decomposition to adjust the error to a noise-free, infinite test set setting. |
| Outcome: | The proposed method outperforms humans and a derived, perfect segment-level annotator in two settings. |
Viability of Machine Translation for Healthcare in Low-Resourced Languages (2025.emnlp-main)
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Hellina Hailu Nigatu, Nikita Mehandru, Negasi Haile Abadi, Blen Gebremeskel, Ahmed Alaa, Monojit Choudhury
| Challenge: | MT errors are more pronounced in low-resourced languages where human translators are scarce and MT tools perform poorly. |
| Approach: | They propose to use a publicly available machine translation system to analyze machine translation errors in healthcare domains. |
| Outcome: | The proposed system reduces errors in two low-resourced languages for healthcare. |
When does Parameter-Efficient Transfer Learning Work for Machine Translation? (2022.emnlp-main)
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| Challenge: | Prior work indicates that parametric fine-tuning methods may not work as well for machine translation (MT). |
| Approach: | They propose to use parameter-efficient fine-tuning methods to adapt large pre-trained models while only tuning a small number of parameters. |
| Outcome: | The proposed methods outperform full fine-tuning for many downstream tasks when the parameter budget corresponds to 10% of the model parameters. |
CHIA: CHoosing Instances to Annotate for Machine Translation (2022.findings-emnlp)
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| Challenge: | Neural machine translation systems perform poorly on low-resource language pairs, for which large-scale parallel data is unavailable. |
| Approach: | They propose a method for selecting instances to annotate for machine translation using existing multi-way parallel datasets. |
| Outcome: | The proposed method outperforms unsupervised methods on 20 languages and a multi-way parallel dataset on high-resource languages. |
Automatic Input Rewriting Improves Translation with Large Language Models (2025.naacl-long)
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| Challenge: | LLMs can rewrite inputs but in machine translation, they are primarily used to re-write outputs via post-editing. |
| Approach: | They propose to use LLMs to rewrite inputs automatically to improve machine translation (MT) they propose to simplify inputs and use quality estimation to assess translatability. |
| Outcome: | The proposed methods can be improved by using quality estimation to assess translatability. |
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. |
| Approach: | They propose a paragraph-level parallel corpus containing verified human translations and 13k evaluated sentences across four language pairs. |
| Outcome: | The proposed corpus compares human evaluations with students and professionals . it shows that the adequacy of human evaluation is controlled by two factors . |
Bilingual Synchronization: Restoring Translational Relationships with Editing Operations (2022.emnlp-main)
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| Challenge: | Neural Machine Translation (NMT) is a one-shot process that generates the target language equivalent of some source text from scratch. |
| Approach: | They propose a machine translation task which assumes an initial target sequence, that must be transformed into a valid translation of the source. |
| Outcome: | The proposed system outperforms other systems trained for similar tasks. |
CLAD-ST: Contrastive Learning with Adversarial Data for Robust Speech Translation (2023.emnlp-main)
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| Challenge: | Cascaded approach is the most popular choice for speech translation, but lacks robustness when dealing with noisy inputs. |
| Approach: | They propose a cascaded approach that uses an automatic speech recognition model and a machine translation model to translate speech in one language to text in another language. |
| Outcome: | The proposed approach achieves significant gains of up to 3 BLEU scores in English-German and English-French speech translation without hurting the translation quality on clean text. |
Evaluating Automatic Subtitling: Correlating Post-editing Effort and Automatic Metrics (2024.lrec-main)
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| Challenge: | Existing metrics for automatic subtitling are not yet fully explored. |
| Approach: | They propose to use machine translation metrics to measure post-editing effort in automatic subtitling to collect data on product-, process- and participant-based data. |
| Outcome: | The proposed metrics correlate with measures of post-editing effort in automatic subtitling. |
Machine Translation Hallucination Detection for Low and High Resource Languages using Large Language Models (2024.findings-emnlp)
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Kenza Benkirane, Laura Gongas, Shahar Pelles, Naomi Fuchs, Joshua Darmon, Pontus Stenetorp, David Adelani, Eduardo Sánchez
| Challenge: | Existing methods for detecting hallucinations in machine translation are limited for low-resource languages. |
| Approach: | They evaluate sentence-level hallucination detection approaches using Large Language Models (LLMs) they find that the choice of model is essential for performance. |
| Outcome: | The proposed models outperform the existing models in HRLs and LRLs on average by 0.16 MCC. |
In-context Examples Selection for Machine Translation (2023.findings-acl)
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| Challenge: | Large-scale generative models can perform a wide range of NLP tasks using in-context learning. |
| Approach: | They aim to understand the properties of good in-context examples for machine translation in both in-domain and out-of-domain settings. |
| Outcome: | The proposed model outperforms a strong kNN-MT baseline in 2 out of 4 out-of-domain datasets. |
Scientific Credibility of Machine Translation Research: A Meta-Evaluation of 769 Papers (2021.acl-long)
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| 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 . |
Uncertainty Determines the Adequacy of the Mode and the Tractability of Decoding in Sequence-to-Sequence Models (2022.acl-long)
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| Challenge: | In many natural language processing tasks the same input can have multiple possible outputs. |
| Approach: | They propose a novel exact n-best search algorithm for neural sequence models to measure sentence-level uncertainty by computing the degree of overlap between references from two different NLP tasks. |
| Outcome: | The proposed algorithm overly spreads the probability mass for uncertain tasks and sentences. |
Evaluation of Machine Translation Performance Across Multiple Genres and Languages (L18-1)
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| Challenge: | In this paper, we evaluate the impact of genre differences on machine translation (MT) for a diverse set of language pairs . BLEU score differences between genres can be large for all genres and all language pairs. |
| Approach: | They use multi-genre benchmarks to evaluate the impact of genre differences on machine translation (MT) they train and use genre classifiers to route test documents to the most appropriate genre systems . |
| Outcome: | The proposed system can improve translation quality for all genres and language pairs . |
Should I Share this Translation? Evaluating Quality Feedback for User Reliance on Machine Translation (2025.emnlp-main)
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| Challenge: | Existing studies on the impact of feedback on human decision-making are limited as people are not equipped to assess the quality of AI predictions. |
| Approach: | They compare the quality of MT inputs and outputs with explicit and implicit feedbacks that directly give users an assessment of translation quality using error highlights and LLM explanations. |
| Outcome: | The proposed model improves decision accuracy and appropriate reliance by using error highlights and explanations, and by using backtranslation and question–answer tables. |
Evaluating Large Language Models for Cross-Lingual Retrieval (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have been evaluated as second-stage reranking models for monolingual IR, but a systematic comparison is lacking for cross-lingual reranked IR. |
| Approach: | They propose to use machine translation to evaluate rerankers in cross-lingual IR . they find that LLMs perform better than LLM-based reranked models . |
| Outcome: | The proposed model improves cross-lingual IR but relies on machine translation for the first stage. |
CoSTA: Code-Switched Speech Translation using Aligned Speech-Text Interleaving (2025.coling-main)
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| Challenge: | More than half of the world's population is presumed to be bilingual . spoken translation of code-switched speech has been under-explored . |
| Approach: | They propose an end-to-end model architecture CoSTA that scaffolds on pretrained ASR and MT modules. |
| Outcome: | The proposed model outperforms existing models by 3.5 BLEU points in spoken translation of code-switched speech. |
Translationese-index: Using Likelihood Ratios for Graded and Generalizable Measurement of Translationese (2025.emnlp-main)
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Yikang Liu, Wanyang Zhang, Yiming Wang, Jialong Tang, Pei Zhang, Baosong Yang, Fei Huang, Rui Wang, Hai Hu
| Challenge: | Translationese is a linguistic property that is often introduced in the translation process that is different from those of original texts. |
| Approach: | They propose to use synthesized translations and translations in the wild to evaluate T-index's generalizability in cross-domain settings and its validity against human judgments. |
| Outcome: | The proposed measure can generalize to unseen genres, authors, and language pairs. |
MMTE: Corpus and Metrics for Evaluating Machine Translation Quality of Metaphorical Language (2024.emnlp-main)
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| Challenge: | Existing evaluation methods focus on fluency and factual reliability, while neglecting figurative quality. |
| Approach: | They propose a set of human evaluation metrics focused on the translation of figurative language and a parallel metaphor corpus generated by post-editing. |
| Outcome: | The proposed evaluation protocol estimates four aspects of MT: Metaphorical Equivalence, Emotion, Authenticity, and Quality. |
GAATME: A Genetic Algorithm for Adversarial Translation Metrics Evaluation (2024.lrec-main)
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| Challenge: | a genetic algorithm generates adversarial translations for arbitrary metrics . produced translations score well in an arbitrary MT evaluation metric, despite serious, deliberately introduced errors. |
| Approach: | They propose a method for decoding translation candidates from a machine translation model via a genetic algorithm to generate adversarial translations to test and challenge MT evaluation metrics. |
| Outcome: | The proposed method scores very well in an arbitrary MT evaluation metric despite serious, deliberately introduced errors. |
Exploring Document-Level Literary Machine Translation with Parallel Paragraphs from World Literature (2022.emnlp-main)
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Katherine Thai, Marzena Karpinska, Kalpesh Krishna, Bill Ray, Moira Inghilleri, John Wieting, Mohit Iyyer
| Challenge: | Literary translation is a culturally significant task, but it is bottlenecked by the small number of qualified literary translators . a dataset of non-English language novels is used to study literary MT . |
| Approach: | They use a dataset of non-English language novels aligned to human and automatic English translations to study literary MT. |
| Outcome: | The proposed model prefers human translations over machine translations at a rate of 84% . state-of-the-art MT metrics do not correlate with preferences, the study finds . |
Accelerating Transformer Inference for Translation via Parallel Decoding (2023.acl-long)
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Andrea Santilli, Silvio Severino, Emilian Postolache, Valentino Maiorca, Michele Mancusi, Riccardo Marin, Emanuele Rodola
| Challenge: | Autoregressive decoding limits the efficiency of transformers for Machine Translation (MT) Existing methods to solve this problem are expensive and require changes to the model. |
| Approach: | They propose to reframe autoregressive decoding with a parallel formulation . they propose to speed up existing models without training or modifications while retaining translation quality. |
| Outcome: | The proposed model speeds up existing models without training or modifications while retaining translation quality. |
IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation (2021.emnlp-main)
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Samuel Cahyawijaya, Genta Indra Winata, Bryan Wilie, Karissa Vincentio, Xiaohong Li, Adhiguna Kuncoro, Sebastian Ruder, Zhi Yuan Lim, Syafri Bahar, Masayu Khodra, Ayu Purwarianti, Pascale Fung
| Challenge: | Lack of publicly available NLG benchmarks for low-resource languages poses a challenge . authors show that IndoBART and IndoGPT achieve competitive performance on all tasks . |
| Approach: | They propose a benchmark to measure natural language generation progress in three low-resource languages of Indonesia . they use a corpus of pretraining datasets to build their models . |
| Outcome: | The proposed benchmark measures progress in Indonesian, Javanese, and Sundanese . the results highlight the importance of pretraining on closely related, localized languages . |
Neural Machine Translation Methods for Translating Text to Sign Language Glosses (2023.acl-long)
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| Challenge: | State-of-the-art techniques common to low resource Machine Translation (MT) are applied to improve MT of spoken language text to Sign Language glosses. |
| Approach: | They propose to use data augmentation, semi-supervised Neural Machine Translation, transfer learning and multilingual NMT to improve MT of spoken language to Sign Language glosses. |
| Outcome: | The proposed models outperform previous work on two German SL corpora and are confirmed by human evaluation. |
Translation in the Hands of Many: Centering Lay Users in Machine Translation Interactions (2025.emnlp-main)
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| Challenge: | Multilingual demands and accessibility have made MT a global tool . however, the understanding of MT consumed by such a diverse group of users remains limited. |
| Approach: | They first trace the evolution of MT user profiles, focusing on non-experts and how their engagement with technology may shift with the rise of LLMs. |
| Outcome: | The proposed approach will help to align MT with user needs and improve the quality of the language. |
Lost in Translation: Benchmarking Commercial Machine Translation Models for Dyslexic-Style Text (2025.findings-acl)
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| Challenge: | Dyslexia affects writing, leading to unique patterns such as letter and homophone swapping. |
| Approach: | They examine the fairness of four commercial machine translation systems towards dyslexic text through a systematic audit using both synthetically generated and real writing from individuals with dyslexia. |
| Outcome: | The proposed system audits show that it is fair to use synthetic and synthetic dyslexic text and real writing from people with dyslexia. |
Physician Detection of Clinical Harm in Machine Translation: Quality Estimation Aids in Reliance and Backtranslation Identifies Critical Errors (2023.emnlp-main)
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| Challenge: | a major challenge in the practical use of Machine Translation (MT) is that users lack information on translation quality to make informed decisions about how to rely on outputs. |
| Approach: | They evaluate quality estimation feedback in vivo with a human study in a medical setting. |
| Outcome: | The proposed method improves appropriate reliance on MT, but backtranslation helps detect harmful errors. |
Learning to Translate by Translating: Stabilizing the Dual Loop via Semantic-Aware Self-Evolution (2026.findings-acl)
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Kui Liu, Mingming Yin, Zuoli Tang, Zihao Li, Chilin Fu, Xiaolu Zhang, Jun Zhou, Lixin Zou, Chenliang Li
| Challenge: | Large Language Models (LLMs) have been successful in machine translation, but lack of high-quality parallel corpora and cost constrain scalability. |
| Approach: | They propose an LLM-driven dual-learning framework that enables autonomous translation . they employ a robust semantic-aware reward function that balances adequacy with reconstruction fidelity . |
| Outcome: | The proposed model outperforms larger models on benchmarks and achieves parity with state-of-the-art supervised baselines on mainstream benchmarks. |
A Benchmark Dataset for Multi-Level Complexity-Controllable Machine Translation (2022.lrec-1)
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Kazuki Tani, Ryoya Yuasa, Kazuki Takikawa, Akihiro Tamura, Tomoyuki Kajiwara, Takashi Ninomiya, Tsuneo Kato
| Challenge: | Existing test datasets for MLCC-MT have three problems: A source language sentence and its simplified target language sentence are not necessarily exactly parallel. |
| Approach: | They propose to use a test dataset to evaluate multi-level complexity-controllable machine translation (MLCC-MT) their results are compared to a standard test dataset constructed from the Newsela corpus . |
| Outcome: | The proposed test dataset is based on the Newsela corpus and is released . it includes automatic filtering, manual check for parallel target language sentences . |
Unsupervised Multimodal Neural Machine Translation with Pseudo Visual Pivoting (2020.acl-main)
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| Challenge: | Unsupervised machine translation (MT) has recently achieved impressive results with monolingual corpora. |
| Approach: | They propose to utilize visual content for disambiguation and promoting latent space alignment in unsupervised machine translation by using multimodal back-translation and pseudo visual pivoting. |
| Outcome: | The proposed model improves over state-of-the-art methods and generalizes well when images are not available at the testing time. |
Tuning Less, Prompting More: In-Context Preference Learning Pipeline for Natural Language Transformation (2025.emnlp-main)
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| Challenge: | Existing approaches to natural language transformation (NLT) tasks face significant challenges, such as the computational costs of leveraging large pre-trained models and the limited generalization ability of fine-tuned smaller models. |
| Approach: | They propose a framework that combines prompting with fine-tuning to enhance smaller models by integrating In-Context Examples from retrieval. |
| Outcome: | The proposed framework outperforms existing methods across MT and TST tasks. |
Impacts of Misspelled Queries on Translation and Product Search (2024.acl-long)
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| Challenge: | In end-to-end e-commerce, the inclusion of a dedicated spelling correction model, and the augmentation of that model’s training data with language-relevant phenomena, each improve robustness and consistency of search results. |
| Approach: | They first analyze the spelling-robustness of a population of machine translation systems and then apply them to a multilingual e-commerce setting to test whether spelling variations affect MT output and user behavior. |
| Outcome: | The proposed model reduces the number of BPE operations and improves spelling-robustness in six languages. |
Balancing Training for Multilingual Neural Machine Translation (2020.acl-main)
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| Challenge: | Existing methods to train multilingual machine translation models are imbalanced and heterogeneous data is wildly varying. |
| Approach: | They propose a method that automatically learns how to weight training data through a data scorer that is optimized to maximize performance on all test languages. |
| Outcome: | The proposed method outperforms baselines on two sets of languages under one-to-many and many-to-1 MT settings and offers flexible control over which languages are optimized. |
What is the Best Way for ChatGPT to Translate Poetry? (2024.acl-long)
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| Challenge: | Despite promising results, our analysis reveals persistent issues in the translations generated by ChatGPT that warrant attention. |
| Approach: | They propose an Explanation-Assisted Poetry Machine Translation method which leverages monolingual poetry explanation as a guiding information for the translation process. |
| Outcome: | The proposed method outperforms traditional translation methods of ChatGPT and the existing online systems in English-Chinese poetry translation. |
Improved Pseudo Data for Machine Translation Quality Estimation with Constrained Beam Search (2023.emnlp-main)
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Xiang Geng, Yu Zhang, Zhejian Lai, Shuaijie She, Wei Zou, Shimin Tao, Hao Yang, Jiajun Chen, Shujian Huang
| Challenge: | evaluating the quality of machine translation outputs becomes increasingly essential with the rapid development of machine language (MT). |
| Approach: | They propose to generate pseudo data using the MT model with constrained beam search (CBSQE) they propose to preserve the reference parts with high MT probabilities as correct translations . |
| Outcome: | The proposed model outperforms strong baselines in both supervised and unsupervised settings. |
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. |
Curriculum Consistency Learning for Conditional Sentence Generation (2024.emnlp-main)
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| Challenge: | Consistency learning (CL) has proven to be a valuable technique for improving the robustness of conditional sentence generation models. |
| Approach: | They propose a strategy that guides models to learn consistency in alignment with their current capacity to differentiate between features. |
| Outcome: | The proposed strategy delivers +2.0 accuracy point improvement compared with vanilla IT and +0.7 COMET scores over traditional CL methods in MT tasks. |
DoCIA: An Online Document-Level Context Incorporation Agent for Speech Translation (2025.findings-acl)
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Xinglin Lyu, Wei Tang, Yuang Li, Xiaofeng Zhao, Ming Zhu, Junhui Li, Yunfei Lu, Min Zhang, Daimeng Wei, Hao Yang, Min Zhang
| Challenge: | Document-level context is crucial for speech translation due to noise from ASR . incorporating document-level contextual information into ST remains a challenge . |
| Approach: | They develop an online framework that integrates document-level context into machine translation . they use document-based modules to integrate document- level context into ST . |
| Outcome: | The proposed framework outperforms baselines in sentence and discourse metrics . it can correct ASR transcription errors and improve translation performance . |
Toucan: Many-to-Many Translation for 150 African Language Pairs (2024.findings-acl)
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| Challenge: | We introduce two language models with 1.2 billion and 3.7 billion parameters to improve Machine Translation (MT) for low-resource languages. |
| Approach: | They propose a set of tools to improve Machine Translation (MT) for low-resource languages with a focus on African languages. |
| Outcome: | The proposed model outperforms existing models on MT for African languages and improves translation evaluation metrics for 1K languages including African languages. |
Paraphrasing as Zero-shot Translation with Feature-guided Diversity Enhancement (2026.acl-long)
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| Challenge: | Existing studies use parallel corpora for training, which results in less diverse paraphrases. |
| Approach: | They train a bidirectional multilingual neural machine translation model on a bilingual parallel corpus and use it as a paraphrasing model. |
| Outcome: | The proposed method generates paraphrases with higher semantic consistency, literal fluency and sentential diversity than existing parabanks and LLMs. |
Indic-TEDST: Datasets and Baselines for Low-Resource Speech to Text Translation (2024.lrec-main)
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| Challenge: | Speech-to-text Translation (ST) tasks are performed by human translators with proficiency in both the source and target languages. |
| Approach: | a new study compares the performance of SOTA ST models on low-resource languages . the authors propose to use a dataset to compare the models on high-resourced languages based on the results of their research . |
| Outcome: | a new study shows that only a few models have performed well on low-resource languages . the results indicate the need for specialized models for low- and high-resourced languages based on the dataset . |
Ties Matter: Meta-Evaluating Modern Metrics with Pairwise Accuracy and Tie Calibration (2023.emnlp-main)
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| Challenge: | Existing variants of Kendall's tau have weaknesses in handling of ties, and in some situations can even be gamed. |
| Approach: | They propose to use Kendall's tau to meta-evaluate machine translation evaluation metrics with a version of pairwise accuracy that gives metrics credit for correctly predicting ties and a tie calibration procedure that automatically introduces ties into metric scores. |
| Outcome: | The proposed model gives metrics credit for correctly predicting ties and introduces ties into metric scores, enabling fair comparison between metrics that do and do not predict ties. |
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 . |
Leveraging GPT-4 for Automatic Translation Post-Editing (2023.findings-emnlp)
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| Challenge: | Neural Machine Translation models still require translation post-editing to rectify errors and enhance quality under critical settings. |
| Approach: | They use GPT-4 to automatically post-edit NMT outputs across several language pairs . they show that GPT4 is adept at translation post- editing, producing meaningful edits . |
| Outcome: | The proposed translation post-editor improves on state-of-the-art language models on English-Chinese, English-German, Chinese-English and German-English language pairs. |
DISCO: A Large Scale Human Annotated Corpus for Disfluency Correction in Indo-European Languages (2023.findings-emnlp)
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| Challenge: | Existing research on disfluency correction has primarily focused on English due to the unavailability of large-scale open-source datasets. |
| Approach: | They propose to use an annotated human-annotated corpus to analyze disfluency correction in four important Indo-European languages to demonstrate the benefits. |
| Outcome: | The proposed model improves BLEU scores by 5.65 points when used with a state-of-the-art machine translation system. |
Prompting PaLM for Translation: Assessing Strategies and Performance (2023.acl-long)
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| Challenge: | Large language models trained on multilingual but not parallel text exhibit remarkable ability to translate between languages. |
| Approach: | They investigate the pathways language model which has demonstrated the strongest machine translation performance among similarly-trained LLMs to date. |
| Outcome: | The pathways language model (PaLM) has demonstrated the strongest machine translation performance among similarly-trained LLMs to date. |
Ladder: A Model-Agnostic Framework Boosting LLM-based Machine Translation to the Next Level (2024.emnlp-main)
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| Challenge: | General-purpose Large Language Models (LLMs) like GPT-4 have exhibited strong translation abilities. |
| Approach: | They propose to use a model-agnostic model to refine the performance of general-purpose large-language models for machine translation (MT) by utilizing Gemma-2B/7B as the backbone. |
| Outcome: | The proposed model-agnostic and cost-effective tool improves the performance of general-purpose large-language models for machine translation (MT) by integrating it with any general-use LLM. |
Hi Guys or Hi Folks? Benchmarking Gender-Neutral Machine Translation with the GeNTE Corpus (2023.emnlp-main)
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| Challenge: | Societal gender asymmetries and inequalities are perpetuated through language . MT often defaults to masculine representations by making undue binary gender assumptions . |
| Approach: | They propose a benchmark and automated evaluation methods to assess gender-neutral translation from English to Italian. |
| Outcome: | The proposed method is based on a survey on gender-neutral translation. |
xTower: A Multilingual LLM for Explaining and Correcting Translation Errors (2024.findings-emnlp)
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Marcos Treviso, Nuno Guerreiro, Sweta Agrawal, Ricardo Rei, José Pombal, Tania Vaz, Helena Wu, Beatriz Silva, Daan Stigt, Andre Martins
| Challenge: | Neural machine translation systems produce translations with errors and anomalies . understanding these errors can help improve the translation quality and user experience . |
| Approach: | They propose an open large language model (LLM) built on top of TowerBase to provide free-text explanations for translation errors in order to guide the generation of a corrected translation. |
| Outcome: | The proposed model improves translation quality and user experience by allowing translators to provide free-text explanations for errors and anomalies. |
“A Little is Enough”: Few-Shot Quality Estimation based Corpus Filtering improves Machine Translation (2023.findings-acl)
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| Challenge: | Quality Estimation (QE) is the task of evaluating the quality of a translation when reference translation is unavailable. |
| Approach: | They propose a Quality Estimation based Filtering approach to extract high-quality parallel data from the pseudo-parallel corpus. |
| Outcome: | The proposed approach improves the machine translation system performance by up to 1.8 BLEU points over the baseline model. |
LAMBDA: Large Language Model-Based Data Augmentation for Multi-Modal Machine Translation (2024.findings-emnlp)
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| Challenge: | Multi-modal machine translation methods are underperforming compared to pre-trained models due to lack of triplet training data. |
| Approach: | They propose a multi-modal machine translation method that integrates images and visual modality to enhance language understanding. |
| Outcome: | The proposed method can enrich the original samples and expand the dataset without requiring external images and text. |
How effective is machine translation on low-resource code-switching? A case study comparing human and automatic metrics (2023.findings-acl)
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| Challenge: | Specifically, we compare the performance of three MT systems in terms of their ability to translate monolingual Vietnamese, a low-resource language, and Vietnamese-English CSW respectively. |
| Approach: | They compare the performance of three machine translation systems in the context of machine translation (MT) they find that state-of-the-art neural translation systems achieve higher scores on automatic metrics when processing CSW input . |
| Outcome: | The proposed system can translate monolingual Vietnamese, a low-resource language, and Vietnamese-English CSW respectively. |
Can Multi-agent Help Disambiguation in Multi-domain Translation? (2026.findings-acl)
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| Challenge: | Existing multi-agent systems have shown strong potential for machine translation (MT) but their performance in multidomain translation remains unsatisfactory due to cross-domain word ambiguity . |
| Approach: | They propose a multi-agent collaborative disambiguation framework for MDT that leverages the collaborative capabilities of LLMs for disambiguations. |
| Outcome: | The proposed framework improves translation performance across multiple domains and improves disambiguation accuracy. |
Non-Autoregressive Document-Level Machine Translation (2023.findings-emnlp)
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| Challenge: | Existing non-autoregressive translation models struggle with document context and handling discourse phenomena. |
| Approach: | They propose a simple but effective design of sentence alignment between source and target to improve their performance on document-level machine translation. |
| Outcome: | The proposed model achieves high acceleration on documents and sentence alignment significantly enhances their performance. |
DialUp! Modeling the Language Continuum by Adapting Models to Dialects and Dialects to Models (2025.acl-long)
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Niyati Bafna, Emily Chang, Nathaniel Romney Robinson, David R. Mortensen, Kenton Murray, David Yarowsky, Hale Sirin
| Challenge: | Recent advances in MT quality and language coverage have shown that language varieties with low baseline performance are more likely to benefit from these approaches. |
| Approach: | They propose a training-time technique for adapting a pretrained model to dialectal data and an inference-time intervention adapting dialectal datasets to the model expertise. |
| Outcome: | The proposed model shows significant performance gains for several dialects from four language families, and modest gains for two other language families. |
What the Harm? Quantifying the Tangible Impact of Gender Bias in Machine Translation with a Human-centered Study (2024.emnlp-main)
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| Challenge: | Existing bias measurements do not reflect the gender disparities found in machine translation. |
| Approach: | They conduct a human-centered study to examine if and to what extent bias in machine translation brings harms with tangible costs, such as quality of service gaps between women and men. |
| Outcome: | The findings advocate for human-centered approaches that can inform the societal impact of bias. |
Enhancing Human Evaluation in Machine Translation with Comparative Judgement (2025.acl-long)
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| Challenge: | Human evaluation is crucial for assessing rapidly evolving language models but is influenced by annotator proficiency and task design. |
| Approach: | They evaluate three annotation setups to integrate comparative judgment into human annotation for machine translation. |
| Outcome: | The proposed approach improves inter-annotator agreement and stability of the annotations. |
Glitter: A Multi-Sentence, Multi-Reference Benchmark for Gender-Fair German Machine Translation (2025.findings-emnlp)
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| Challenge: | Existing MT models are limited in size and often consist of single sentences or single gender-fair formulation types. |
| Approach: | They propose a benchmark for machine translation that features extended passages with professional translations implementing gender-fair alternatives: neutral rewording, typographical solutions and neologistic forms. |
| Outcome: | The proposed benchmark features extended passages with professional translations implementing three gender-fair alternatives: neutral rewording, typographical solutions (gender star), and neologistic forms (-ens forms). |
MT-RewardTree: A Comprehensive Framework for Advancing LLM-Based Machine Translation via Reward Modeling (2025.findings-emnlp)
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| Challenge: | MT-RewardTree provides a framework for constructing, evaluating, and deploying process reward models in machine translation (MT) |
| Approach: | They propose a method for automatically generating token-level preference pairs using approximate Monte Carlo Tree Search. |
| Outcome: | The proposed framework achieves state-of-the-art performance in token-level evaluation and sequence-level analysis. |
MT-R1-Zero: Advancing LLM-based Machine Translation via R1-Zero-like Reinforcement Learning (2025.findings-emnlp)
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| Challenge: | Large-scale reinforcement learning (RL) methods have proven effective in enhancing the reasoning abilities of large language models. |
| Approach: | They propose an open-source adaptation of the R1-Zero RL framework for machine translation (MT) their code is available at https://github.com/fzp0424/MT-R1-zero. |
| Outcome: | The proposed framework surpasses towerinstruct-7B-v0.2 on the english-chinese benchmark by 1.26 points. |
Languages Still Left Behind: Toward a Better Multilingual Machine Translation Benchmark (2025.emnlp-main)
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Chihiro Taguchi, Seng Mai, Keita Kurabe, Yusuke Sakai, Georgina Agyei, Soudabeh Eslami, David Chiang
| Challenge: | Multilingual machine translation (MT) benchmarks are widely used to evaluate the capabilities of modern MT systems. |
| Approach: | They propose to use a multilingual machine translation benchmark to assess the capabilities of modern machine translation systems. |
| Outcome: | The FLORES+ benchmark claims to maintain a translation quality score of over 90% . however, the data in four languages falls short of the 90% quality standard . |
CLewR: Curriculum Learning with Restarts for Machine Translation Preference Learning (2026.findings-acl)
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| Challenge: | Large language models (LLMs) have demonstrated competitive performance in zero-shot multilingual machine translation (MT). |
| Approach: | They propose a curriculum learning strategy with restarts which reiterates easy-to-hard curriculum multiple times during training to effectively mitigate catastrophic forgetting of easy examples. |
| Outcome: | The proposed model replicates easy-to-hard curriculum multiple times during training to mitigate catastrophic forgetting of easy examples. |
Document-Level Machine Translation with Large Language Models (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) such as ChatGPT can produce coherent, cohesive, relevant, and fluent answers for various natural language processing tasks. |
| Approach: | They examine the impact of different prompts on document-level translation quality and discourse phenomena using figures and lines, which are invisible to GPT-4. |
| Outcome: | The proposed models outperform commercial MT systems and advanced document-level MT methods on a number of benchmarks and show potential to become a new paradigm for document- level translation. |
Breaking Consensus Bias: Unsupervised Reinforcement Learning for Machine Translation (2026.findings-acl)
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| Challenge: | Existing RL approaches for MT face fixed references or the production of homogeneous references leading to mode collapse in unsupervised settings. |
| Approach: | They propose an Entropy-Driven Unsupervised RL framework for machine translation that leverages entropy for supervision construction and self-evolution. |
| Outcome: | The proposed framework outperforms supervised and unsupervised baselines in multiple language pairs. |
Layer-aware Dual-directional Modulation for Low-resource Machine Translation (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated excellent performance in Machine Translation (MT) however, a performance gap persists between high-resource and low-resourced languages due to imbalanced pre-training data. |
| Approach: | They propose a layer-wise metric to quantify the activation divergence between high- and low-resource languages. |
| Outcome: | The proposed model outperforms standard LoRA fine-tuning on Chinese-to-seven low-resource language translations. |
TransAlign: Machine Translation Encoders are Strong Word Aligners, Too (2025.findings-emnlp)
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| Challenge: | translation-based approaches to cross-lingual transfer (XLT) are limited. |
| Approach: | They propose a word aligner that utilizes the encoder of a massively multilingual MT model. |
| Outcome: | The proposed word aligner outperforms existing WA and state-of-the-art non-WA-based methods in token classification tasks. |
Beyond Correlation: Interpretable Evaluation of Machine Translation Metrics (2024.emnlp-main)
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| Challenge: | Recent studies have shown that MT metrics return assessments as scalar scores that are difficult to interpret, posing a challenge to making informed design choices. |
| Approach: | They propose an interpretable evaluation framework that evaluates MT metrics in two scenarios that serve as proxies for filtering and translation re-ranking use cases. |
| Outcome: | The proposed framework offers clearer insights than correlation with human judgments. |
Revisiting Metric Reliability for Fine-grained Evaluation of Machine Translation and Summarization in Indian Languages (2026.acl-long)
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| 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. |
Data Augmentation for Maltese NLP using Transliterated and Machine Translated Arabic Data (2025.findings-emnlp)
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| Challenge: | Maltese is a Semitic language that has evolved under extensive influence from Romance and Germanic languages, particularly Italian and English. |
| Approach: | They investigate whether Arabic-language resources can support Maltese natural language processing . they introduce transliteration schemes and machine translation approaches to align Arabic text with Maltesen . |
| Outcome: | The proposed techniques can significantly improve Maltese natural language processing tasks. |
Compositional Translation: A Novel LLM-based Approach for Low-resource Machine Translation (2025.findings-emnlp)
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| Challenge: | generative large language models (LLMs) can perform in-context learning . machine translation (MT) has been shown to benefit from in-constitu examples . |
| Approach: | They propose a compositional translation paradigm that replaces naive few-shot MT with similarity-based demonstrations. |
| Outcome: | The proposed paradigm replaces naive few-shot MT with similarity-based demonstrations. |
TopXGen: Topic-Diverse Parallel Data Generation for Low-Resource Machine Translation (2025.findings-emnlp)
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| Challenge: | In-context learning and similarity search have been shown to improve LLMs' performance in machine translation, but they lag behind when dealing with low-resource languages. |
| Approach: | They propose a method that uses an LLM to generate topic-specific target-side data in the LRL. |
| Outcome: | The proposed approach boosts LLM translation performance during in-context learning and fine-tuning. |
Revisiting Context Choices for Context-aware Machine Translation (2024.lrec-main)
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| Challenge: | Recent work has cast doubt on whether context-aware machine translation models learn useful signals from context or are improvements in automatic evaluation metrics just a side-effect. |
| Approach: | They propose to use separate encoders for source sentence and context as multiple sources for one target sentence to train context-aware machine translation models. |
| Outcome: | The proposed model improves translation quality even with empty lines as context, but the correct context improves it and random out-of-domain context degrades it. |
Watching the Watchers: Exposing Gender Disparities in Machine Translation Quality Estimation (2025.acl-long)
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| Challenge: | Qualitative estimation (QE) metrics have been optimized to align with human quality judgments, but whether they encode social biases has been largely overlooked. |
| Approach: | They define and investigate gender bias of QE metrics and discuss its downstream implications for machine translation (MT) when a human entity’s gender in the source is undisclosed, masculine-inflected translations score higher than feminine-infflectes translations are penalized. |
| Outcome: | The proposed measures are based on gender-based quality estimation metrics across multiple domains, datasets, and languages. |
Don’t Sweat the Small Stuff: Segment-Level Meta-Evaluation Based on Pairwise Difference Correlation (2025.emnlp-main)
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| Challenge: | Existing methods to calculate agreement are acceq and acc eq, but there are many ways to compute agreement based on how scores are grouped together. |
| Approach: | They propose a segment-level meta-evaluation metric that utilizes pairwise differences rather than raw scores to refine Global Pearson to intra-segment comparisons. |
| Outcome: | The proposed metric correctly ranks sentinel evaluation metrics and better aligns with human error weightings than acceq. |
Make Imagination Clearer! Stable Diffusion-based Visual Imagination for Multimodal Machine Translation (2025.acl-long)
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Andong Chen, Yuchen Song, Kehai Chen, Xuefeng Bai, Muyun Yang, Liqiang Nie, Jie Liu, Tiejun Zhao, Min Zhang
| Challenge: | Experimental results show that our model significantly outperforms existing multimodal MT and text-only MT. |
| Approach: | They propose a stable diffusion-based imagination network into a multimodal large language model to generate an image for each source sentence. |
| Outcome: | The proposed model outperforms existing multimodal and text-only MT and achieves an average improvement of 14 BLEU points on Multi30K and MSCOCO multimodal MT benchmarks. |
Exploring Context Strategies in LLMs for Discourse-Aware Machine Translation (2025.findings-emnlp)
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| Challenge: | Large language models excel at machine translation, but the impact of how LLMs utilize different forms of contextual information on discourse-level phenomena remains underexplored. |
| Approach: | They examine how different forms of context influence standard MT metrics and specific discourse phenomena such as formality, pronoun selection, and lexical cohesion. |
| Outcome: | Evaluating multiple LLMs across multiple domains and language pairs, the findings consistently show that context boosts translation and discourse-specific performance. |
BridG MT: Enhancing LLMs’ Machine Translation Capabilities with Sentence Bridging and Gradual MT (2025.findings-acl)
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| Challenge: | Recent Large Language Models (LLMs) have demonstrated impressive translation performance without the need for fine-tuning on additional parallel corpora. |
| Approach: | They propose a method that combines Sentence Bridging and Gradual MT to enhance LLMs' translation performance even outperforming translation methods that rely on a large number of few-shot examples. |
| Outcome: | The proposed method outperforms translation methods that rely on a large number of few-shot examples even when the source and target languages are low-resource languages. |
MT-OSC: Path for LLMs that Get Lost in Multi-Turn Conversation (2026.findings-acl)
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Jyotika Singh, Fang Tu, Miguel Ballesteros, Weiyi Sun, Sandip Ghoshal, Michelle Yuan, Yassine Benajiba, Sujith Ravi, Dan Roth
| Challenge: | Large language models suffer performance degradation when user instructions and context are distributed over multiple conversational turns. |
| Approach: | They propose a framework that condenses chat history in the background without disrupting the user experience. |
| Outcome: | The proposed framework reduces token counts by up to 72% in 10-turn dialogues while remaining robust to distractors and irrelevant turns. |
TIGQA: An Expert-Annotated Question-Answering Dataset in Tigrinya (2024.lrec-main)
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| Challenge: | Existing annotated datasets for NLP tasks in languages with limited resources are limited. |
| Approach: | They propose to use machine translation to convert existing Tigrinya dataset into a Tigrina dataset in SQuAD format. |
| Outcome: | The proposed dataset is an expert-annotated Tigrinya dataset with 2,685 question-answer pairs covering 122 diverse topics. |
Scaling Low-Resource MT via Synthetic Data Generation with LLMs (2025.emnlp-main)
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Ona de Gibert, Joseph Attieh, Teemu Vahtola, Mikko Aulamo, Zihao Li, Raúl Vázquez, Tiancheng Hu, Jörg Tiedemann
| Challenge: | a recent study has shown that LLM-generated synthetic data can improve low-resource machine translation performance . traditional data augmentation techniques like back-translation preserve the human-written target and synthesize the other . |
| Approach: | They construct a document-level synthetic corpus from English Europarl and extend it via pivoting to 147 additional language pairs. |
| Outcome: | The proposed model can significantly improve low-resource machine translation performance even when noisy. |
Towards Robust In-Context Learning for Machine Translation with Large Language Models (2024.lrec-main)
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| Challenge: | Experimental results demonstrate the effectiveness of our method, particularly in domain adaptation. |
| Approach: | They propose a method to retrieve translation pairs as demonstrations from an additional datastore to guide translation without updating the LLMs. |
| Outcome: | The proposed method reduces noise and improves translation performance in domain adaptation. |
How Much Do LLMs Hallucinate across Languages? On Realistic Multilingual Estimation of LLM Hallucination (2025.emnlp-main)
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| Challenge: | despite LLMs becoming increasingly multilingual, most studies on detecting and quantifying LLM hallucination are English-centric . |
| Approach: | They train a multilingual hallucination detection model and conduct a large-scale study across 30 languages and 6 open-source LLM families. |
| Outcome: | The proposed model is based on an English-centric model and annotates gold data for five high-resource languages. |
Alexandria: A Multi-Domain Dialectal Arabic Machine Translation Dataset for Culturally Inclusive and Linguistically Diverse LLMs (2026.acl-long)
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Abdellah EL Mekki, Samar M. Magdy, Houdaifa Atou, Ruwa AbuHweidi, Baraah Qawasmeh, Omer Nacar, Thikra Al-hibiri, Razan Saadie, Hamzah A. Alsayadi, Nadia Ghezaiel Hammouda, Alshima Mohammed Alkhazimi, Aya Hamod, Al-Yas Yaqoob Al-Ghafri, Wesam El-Sayed, Asila Ismail al Sharji, Mohamad Ballout, Anas Belfathi, Karim Ghaddar, Serry Sibaee, Alaa Aoun, Aeej Mohammed Aseri, Lina Abureesh, Ahlam Bashiti, Majdal Yousef, Abdulaziz Hafiz, Yehdih Mohamed, Emira Hamedtou, Brakehe Emehah, Rahaf Alhamouri, Youssef Nafea, Aya El Aatar, Walid Al-Dhabyani, Emhemed S. Hamed, Sara Shatnawi, Fakhraddin Alwajih, Khalid Elkhidir, Ashwag Alasmari, Abdurrahman Gerrio, Omar Said Alshahri, AbdelRahim A. Elmadany, Ismail Berrada, Amir Azad Adli Al-kathiri, Fadi Zaraket, Mustafa Jarrar, Yahya Mohamed EL Hadj, Hassan Alhuzali, Muhammad Abdul-Mageed
| Challenge: | Arabic is a highly diglossic language where most daily communication occurs in regional dialects rather than modern standard Arabic (MSA). |
| Approach: | They propose a large-scale, community-driven, human-translated dataset to bridge this gap . Alexandria covers 13 Arab countries and 11 high-impact domains . it provides unprecedented granularity by associating contributions with city-of-origin metadata . |
| Outcome: | The Alexandria dataset covers 13 Arab countries and 11 high-impact domains . it provides unprecedented granularity by associating contributions with city-of-origin metadata . Alexandria is a training resource and a rigorous benchmark for evaluating MT and LLMs based on the Alexandria dataset . |
It’s Not a Walk in the Park! Challenges of Idiom Translation in Speech-to-text Systems (2025.acl-long)
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| Challenge: | idioms are defined as words with a figurative meaning not deducible from their individual components. |
| Approach: | They compare idiom translation as compared to conventional news translation in two languages . they compare MT and SLT systems with MT, Large Language Models and cascaded alternatives . |
| Outcome: | The proposed systems show better handling of idioms than standard news translation systems. |
AfroCS-xs: Creating a Compact, High-Quality, Human-Validated Code-Switched Dataset for African Languages (2025.acl-long)
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Kayode Olaleye, Arturo Oncevay, Mathieu Sibue, Nombuyiselo Zondi, Michelle Terblanche, Sibongile Mapikitla, Richard Lastrucci, Charese Smiley, Vukosi Marivate
| Challenge: | AfroCS-xs is a low-quality dataset for code-switching in multilingual communities . code-witching is prevalent in multicultural societies but lacks high-quality data for model development . |
| Approach: | They propose to use human-validated synthetic code-switched datasets to generate code-witched sentences for four African languages and English within a specific domain—agriculture. |
| Outcome: | The proposed model improves translation accuracy on the high-quality dataset for four African languages and English within a specific domain—agriculture. |
Toward Machine Translation Literacy: How Lay Users Perceive and Rely on Imperfect Translations (2025.emnlp-main)
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Yimin Xiao, Yongle Zhang, Dayeon Ki, Calvin Bao, Marianna J. Martindale, Charlotte Vaughn, Ge Gao, Marine Carpuat
| Challenge: | Using machine translation tools for everyday tasks is becoming more commonplace, but a lack of evaluation strategies and alternatives can cause users to over-rely on it. |
| Approach: | They propose to use MT evaluation techniques to promote MT quality and MT literacy among its users. |
| Outcome: | The findings highlight the need for evaluation and NLP explanation techniques to promote MT quality and MT literacy among its users. |
Liaozhai through the Looking-Glass: On Paratextual Explicitation of Culture-Bound Terms in Machine Translation (2025.emnlp-main)
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| Challenge: | Existing approaches to explicitating culturally-embedded meaning have focused on in-text solutions, overlooking paratextual apparatus in the footnotes and endnotes employed by professional translators. |
| Approach: | They formalize Genette's (1987) theory of paratexts and evaluate expert-aligned paratext models . they find that LLM-generated paratext improves audience comprehension . |
| Outcome: | The proposed model improves the comprehension of the Chinese short story Liaozhai by using human evaluations. |
Translating Domain-Specific Terminology in Typologically-Diverse Languages: A Study in Tax and Financial Education (2025.emnlp-main)
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| Challenge: | Existing public terminology datasets for MT research are limited in language coverage or domain specificity, making it difficult to assess or improve MT systems in specialized settings. |
| Approach: | They propose a multilingual terminology resource for tax and financial education covering seven typologically diverse languages: English, Spanish, Russian, Vietnamese, Korean, Chinese (traditional and simplified) and Haitian Creole. |
| Outcome: | The proposed terminology resource covers seven typologically diverse languages: English, Spanish, Russian, Vietnamese, Korean, Chinese (traditional and simplified) and Haitian Creole. |
Measuring User’s Mental Models of Speech Translation in Human-AI Collaboration (2026.acl-long)
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| Challenge: | Existing research on machine translation tools has not revealed how users perceive MT errors and how they evolve through interaction. |
| Approach: | They propose a framework where users accept MT output or request professional re-translation to answer questions based on information presented in a foreign language. |
| Outcome: | The proposed framework can predict where the system is likely to be wrong and how it evolves through interaction. |
PEAR: Pairwise Evaluation for Automatic Relative Scoring in Machine Translation (2026.acl-long)
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| Challenge: | PEAR is a supervised quality estimation metric that reframes reference-free machine translation evaluation as a graded pairwise comparison. |
| Approach: | They propose to use a supervised quality estimation metric family to reframe machine translation evaluation as a graded pairwise comparison. |
| Outcome: | The proposed metric outperforms strictly matched single-candidate QE baselines on the WMT24 meta-evaluation benchmark. |
LQM: Linguistically Motivated Multidimensional Quality Metrics for Machine Translation (2026.findings-acl)
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| Challenge: | Existing MT evaluation frameworks fail to capture dialect- and culture-specific errors in diglossic languages. |
| Approach: | They propose a hierarchical error taxonomy for diagnosing MT errors through six linguistic levels: sociolinguistics, pragmatics, semantics, morphosyntax, orthography, and graphetics. |
| Outcome: | The proposed framework produces 6,113 labeled error spans across 3,495 unique erroneous sentences . it is language-agnostic and can be easily applied to or adapted for other languages. |