Papers with MT system
Upping the Ante: Towards a Better Benchmark for Chinese-to-English Machine Translation (L18-1)
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| Challenge: | Currently, there is no widely accepted standard for evaluation of machine translation (MT) for Chinese-to-English translation, there are no standard for standardized training sets, development sets, and test sets. |
| Approach: | They propose to use Chinese-to-English machine translation as a benchmark . they build a highly competitive state-of-the-art MT system that outperforms reported results . |
| Outcome: | The proposed system outperforms reported results on NIST OpenMT test sets in almost all papers published in major conferences and journals in computational linguistics and artificial intelligence in the past 11 years. |
Machine translation impact in E-commerce multilingual search (2022.emnlp-industry)
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| Challenge: | Existing studies have demonstrated that cross-lingual information retrieval performance is highly dependent on query translation quality. |
| Approach: | They investigate whether improving query translation quality yields little or no benefit to further improve retrieval performance. |
| Outcome: | The proposed methods compare query translations for multiple language pairs and identify the most promising language pairs to invest and improve. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
XNLIeu: a dataset for cross-lingual NLI in Basque (2024.naacl-long)
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| Challenge: | XNLI is a popular benchmark used to evaluate cross-lingual Natural Language Understanding (NLU) in languages such as English, Basque and other low-resource languages. |
| Approach: | They expand XNLI to include Basque, a low-resource language that can benefit from transfer-learning approaches. |
| Outcome: | The proposed dataset includes Basque, a low-resource language that can benefit from transfer-learning approaches. |
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. |
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. |
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. |
Refer to the Reference: Reference-focused Synthetic Automatic Post-Editing Data Generation (2025.coling-main)
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| Challenge: | Existing approaches to synthetic APE data generation use source (src) sentences in a parallel corpus to obtain translations (mt) through an MT system and treat corresponding reference (ref) sentences as post-edits (pe). |
| Approach: | They propose a reference-focused synthetic APE data generation technique that uses ‘ref’ instead of src’ sentences to obtain corrupted translations. |
| Outcome: | The proposed technique improves on English-German, English-Russian, English -Marathi, English and Hindi language pairs. |
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. |
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. |
Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation (2021.findings-emnlp)
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| Challenge: | Quality Estimation (QE) is an essential role in applications of Machine Translation (MT). |
| Approach: | They propose to fuse uncertainty quantification into a pre-trained cross-lingual language model to predict the translation quality. |
| Outcome: | The proposed method achieves state-of-the-art on the datasets of WMT 2020 QE shared task. |
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. |
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. |
PreQuEL: Quality Estimation of Machine Translation Outputs in Advance (2022.emnlp-main)
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| Challenge: | A PreQuEL system predicts how well a given sentence will be translated without recourse to the actual translation. |
| Approach: | They propose a task that uses a model to predict how well a given sentence will be translated . they show that the model is sensitive to syntactic and semantic distinctions . |
| Outcome: | The proposed model improves on the Quality-Estimation task and on challenge sets and languages. |