Papers with machine translation models
Membership Inference Attacks on Sequence-to-Sequence Models: Is My Data In Your Machine Translation System? (2020.tacl-1)
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| Challenge: | Data privacy is an important issue for “machine learning as a service” providers. |
| Approach: | They propose an attack on membership inference attacks using a sequence-to-sequence model and a machine translation dataset to investigate the feasibility of a privacy attack. |
| Outcome: | The proposed model can infer sentence-level membership from the output of the model, but it is difficult to infer it. |
Opportunities for Human-centered Evaluation of Machine Translation Systems (2022.findings-naacl)
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| Challenge: | a new study examines the role of machine translation in larger user-facing systems . a sysadmin and a human factors researcher are developing evaluation tools . |
| Approach: | They argue that machine translation models are embedded in larger user-facing systems . they argue that evaluation at the systems level is still lacking . |
| Outcome: | The proposed model evaluations are based on human-computer interaction models . the authors argue that evaluations should be based more on the entire system . |
GATITOS: Using a New Multilingual Lexicon for Low-resource Machine Translation (2023.emnlp-main)
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| Challenge: | a new study explores the effectiveness of bilingual lexica in machine translation models . cross-lingual vocabulary alignment is still highly imperfect in these models, despite the success of supervised and self-supervised training. |
| Approach: | They use a resource to improve translation performance on 200-language models . they show that lexica is more reliable than human-translated data . |
| Outcome: | The proposed approach improves on 200-language translation models with lexical data augmentation . the proposed approach is open-source and has 168 tail languages . |
Guiding Zero-Shot Paraphrase Generation with Fine-Grained Control Tokens (2023.starsem-1)
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| Challenge: | Sequence-to-sequence paraphrase generation models struggle with the generation of diverse paraphrases. |
| Approach: | They propose a translation-based guided paraphrase generation model that learns useful features for promoting surface form variation in generated paraphrases from cross-lingual parallel data. |
| Outcome: | The proposed model learns useful features for promoting surface form variation in generated paraphrases from cross-lingual parallel data. |
Improving Grammatical Error Correction with Machine Translation Pairs (2020.findings-emnlp)
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| Challenge: | Existing methods to generate error-corrected sentence pairs for improving grammatical error correction are not available. |
| Approach: | They propose a method to generate error-corrected sentence pairs for improving grammatical error correction based on machine translation models of different qualities . |
| Outcome: | The proposed method can generate multiple error-corrected sentence pairs from Chinese to English text. |
Inseq: An Interpretability Toolkit for Sequence Generation Models (2023.acl-demo)
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| Challenge: | Recent studies focused on classification tasks while largely overlooking generation settings due to a lack of dedicated tools. |
| Approach: | They propose to use Inseq to democratize access to interpretability analyses of sequence generation models by enabling intuitive extraction of models’ internal information and feature importance scores for popular decoder-only and encoder-decoder Transformers architectures. |
| Outcome: | The proposed library can extract models’ internal information and feature importance scores for popular decoder-only and encoder-decoder Transformers architectures. |
Better Quality Estimation for Low Resource Corpus Mining (2022.findings-acl)
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| Challenge: | State-of-the-art Quality Estimation models lack robustness to out-of domain examples. |
| Approach: | They propose a method that uses multitask training, data augmentation and contrastive learning to achieve better and more robust QE performance. |
| Outcome: | The proposed method improves QE performance significantly in the MLQE challenge and the robustness of QE models when tested in the Parallel Corpus Mining setup. |
How Suitable Are Subword Segmentation Strategies for Translating Non-Concatenative Morphology? (2021.findings-emnlp)
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| Challenge: | Data-driven subword segmentation is the default strategy for open-vocabulary machine translation but may not be sufficiently generic for learning non-concatenative morphology. |
| Approach: | They propose to test data-driven subword segmentation on non-concatenative morphological phenomena in a controlled, semi-synthetic setting. |
| Outcome: | The proposed model can translate non-concatenative morphological phenomena in a controlled, semi-synthetic setting. |
An End-to-End Contrastive Self-Supervised Learning Framework for Language Understanding (2022.tacl-1)
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| Challenge: | Existing approaches to learning data representations using contrastive learning perform data augmentation and contrastive training separately. |
| Approach: | They propose a framework that performs data augmentation and contrastive learning end-to-end . they propose to combine data augmented with text encoders to optimize for contrastive training . |
| Outcome: | Experiments on GLUE and Gururangan datasets show the proposed framework is effective in NLP. |
DEEP: DEnoising Entity Pre-training for Neural Machine Translation (2022.acl-long)
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| Challenge: | Earlier named entity translation methods focus on phonetic transliteration, which ignores the sentence context for translation. |
| Approach: | They propose a DEnoising Entity Pre-training method that leverages monolingual data and a knowledge base to improve named entity translation accuracy within sentences. |
| Outcome: | The proposed method improves on three language pairs and denoising auto-encoding baselines. |
Non-Autoregressive Machine Translation: It’s Not as Fast as it Seems (2022.naacl-main)
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| Challenge: | Efficient machine translation models are commercially important as they can increase inference speeds, reduce costs and carbon emissions. |
| Approach: | They compare NAR models with autoregressive models to evaluate their performance . they point out flaws in evaluation methodology and argue for consistent evaluation . |
| Outcome: | The proposed model is faster on GPUs, but slower under more realistic usage conditions. |
Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation (2021.emnlp-main)
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| Challenge: | a series of experiments show that fine-tuning only the cross-attention parameters is nearly as effective as fine-timing all parameters. |
| Approach: | They conduct experiments to fine-tune a translation model on data where either the source or target language has changed. |
| Outcome: | The proposed model can be trained to several new languages with reduced parameter storage overhead. |
Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translation (2021.findings-emnlp)
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| Challenge: | Recent studies have found evidence of gender bias in machine translation and coreference resolution models using mostly synthetic diagnostic datasets. |
| Approach: | They propose a semi-automatic method to vastly extend synthetic, small diagnostic datasets to include grammatical patterns indicating stereotypical and non-stereotypical gender-role assignments. |
| Outcome: | The proposed method extends the existing dataset to 108K diverse English sentences. |
Exploiting Biased Models to De-bias Text: A Gender-Fair Rewriting Model (2023.acl-long)
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| Challenge: | Existing work has explored using sequence-to-sequence rewriting models to transform biased outputs into more gender-fair language by creating pseudo training data through linguistic rules. |
| Approach: | They propose to use machine translation models to create gender-biased text from real gender-fair text via round-trip translation to eliminate rule-based data creation. |
| Outcome: | The proposed approach matches the performance of state-of-the-art rewriting models for English. |
Improving Transformer Models by Reordering their Sublayers (2020.acl-main)
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| Challenge: | a sandwich transformer pattern is a new approach to multilayer transformers that can be used for different tasks. |
| Approach: | They propose a transformer ordering pattern that reorders sublayers in a sandwich transformer pattern . they generate random transformer models and train them with the language modeling objective . |
| Outcome: | The proposed pattern improves perplexity on multiple word-level and character-level language modeling benchmarks at no cost in parameters, memory, or training time. |
DecoderLens: Layerwise Interpretation of Encoder-Decoder Transformers (2024.findings-naacl)
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| Challenge: | Existing interpretability methods have been proposed to interpret the inner workings of Transformer models at different levels of precision and complexity. |
| Approach: | They propose a method to analyze encoder-decoder Transformers by using the decoder module Model Output encoder to cross-attend representations of intermediate encoder activations instead of using the default output. |
| Outcome: | The proposed method maps uninterpretable representations to human-interpreted sequences of words or symbols, shedding new light on the information flow in this popular but understudied class of 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. |
Interactive Post-Editing for Verbosity Controlled Translation (2022.coling-1)
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| Challenge: | Recent machine translation models have shown to excel with aspects of translation quality like adequacy and fluency but these models still suffer notable shortcomings like out-of-domain data, low-resource languages, rare words and longer sentences. |
| Approach: | They propose to use human-in-loop interactive post-editing models to improve translation quality and rephrase the text with a desired style variation. |
| Outcome: | The proposed model achieves BERTScore over state-of-the-art machine translation models while maintaining the desired token-level and verbosity preference. |
CCMatrix: Mining Billions of High-Quality Parallel Sentences on the Web (2021.acl-long)
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| Challenge: | Using a curated common crawl corpus, we were able to mine 10.8 billion parallel sentences out of which only 2.9 billions are aligned with English. |
| Approach: | They use 32 snapshots of a curated common crawl corpus totaling 71 billion unique sentences to mine 10.8 billion parallel sentences out of which only 2.9 billions are aligned with English. |
| Outcome: | The proposed system outperforms the best single systems on the WMT’19 test set for English-German/Russian/Chinese and outperformed the best submission at the 2020 WAT workshop. |
Multilingual and Cross-Lingual Intent Detection from Spoken Data (2021.emnlp-main)
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Daniela Gerz, Pei-Hao Su, Razvan Kusztos, Avishek Mondal, Michał Lis, Eshan Singhal, Nikola Mrkšić, Tsung-Hsien Wen, Ivan Vulić
| Challenge: | a systematic study on multilingual and cross-lingual intent detection from spoken data is presented . current work on intent detection is limited to English, and standard benchmarks exist only in English. |
| Approach: | They present a systematic study on multilingual and cross-lingual intent detection from spoken data. |
| Outcome: | The proposed resource is called MInDS-14, and it provides strong intent detection in most target languages. |
Leveraging Synthetic Targets for Machine Translation (2023.findings-acl)
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| Challenge: | Using synthetic target data, training models on synthetic targets outperforms training on actual ground-truth data. |
| Approach: | They propose a recipe for training machine translation models on synthetic target data by leveraging a large pre-trained model. |
| Outcome: | The proposed model outperforms training on real-world translation datasets. |
Mixture-of-Supernets: Improving Weight-Sharing Supernet Training with Architecture-Routed Mixture-of-Experts (2024.findings-acl)
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Ganesh Jawahar, Haichuan Yang, Yunyang Xiong, Zechun Liu, Dilin Wang, Fei Sun, Meng Li, Aasish Pappu, Barlas Oguz, Muhammad Abdul-Mageed, Laks Lakshmanan, Raghuraman Krishnamoorthi, Vikas Chandra
| Challenge: | Neural architecture search (NAS) uses weight-sharing supernets to generate diverse subnetworks without retraining. |
| Approach: | They propose a weight-sharing supernet that leverages mixture-of-experts to enhance supernet model expressiveness with minimal training overhead. |
| Outcome: | The proposed method achieves state-of-the-art (SoTA) performance in NAS for fast machine translation models, surpassing NAS-BERT and AutoDistil across various model sizes. |
Augmenting Large Language Model Translators via Translation Memories (2023.findings-acl)
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Yongyu Mu, Abudurexiti Reheman, Zhiquan Cao, Yuchun Fan, Bei Li, Yinqiao Li, Tong Xiao, Chunliang Zhang, Jingbo Zhu
| Challenge: | Using translation memories (TMs) as prompts is a promising approach to in-context learning of machine translation models. |
| Approach: | They propose to use translation memories (TMs) as prompts to prompt large language models (LLMs) they find that the ability of LLMs to "understand" prompts is helpful . |
| Outcome: | The results are comparable to state-of-the-art NMT systems with bilingual data and are tuned on downstream tasks. |
Graph Algorithms for Multiparallel Word Alignment (2021.emnlp-main)
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Ayyoob ImaniGooghari, Masoud Jalili Sabet, Lutfi Kerem Senel, Philipp Dufter, François Yvon, Hinrich Schütze
| Challenge: | Word alignments are useful for typological research and can be used in machine translation systems. |
| Approach: | They propose to exploit the multiparallelity of parallel corpora by representing bilingual alignments as a graph and then predicting additional edges. |
| Outcome: | The proposed algorithm improves the accuracy of bilingual alignments by 28% over baseline algorithms. |
JParaCrawl v3.0: A Large-scale English-Japanese Parallel Corpus (2022.lrec-1)
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| Challenge: | Existing parallel corpora for English-Japanese are limited, limiting the accuracy of machine translation models. |
| Approach: | They propose a web-based English-Japanese parallel corpus with 21 million unique sentence pairs . this is more than twice as many as the previous corpus JParaCrawl v2.0 . |
| Outcome: | The proposed corpus boosts the accuracy of machine translation models on various domains. |
Addressing Posterior Collapse with Mutual Information for Improved Variational Neural Machine Translation (2020.acl-main)
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| Challenge: | Existing variational inference models ignore their latent variables, a phenomenon called posterior collapse. |
| Approach: | They propose a new loss function for conditional variational autoencoders that counteracts posterior collapse by using a modified evidence lower bound objective and a factorized decoder. |
| Outcome: | The proposed model yields improved translation quality compared to existing models on WMT RoEn and DeEn. |
Parallel Corpus Filtering via Pre-trained Language Models (2020.acl-main)
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| Challenge: | Existing methods to filter out noisy parallel sentences from web crawled data are in demand. |
| Approach: | They propose a method to filter out noisy sentence pairs from web crawled corpora using pre-trained language models. |
| Outcome: | The proposed method outperforms baselines and achieves state-of-the-art on two datasets. |
Dynamic Jointly Batch Selection for Data Efficient Machine Translation Fine-Tuning (2025.emnlp-main)
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| Challenge: | Data quality and effective selection are key to improving machine translation performance . study focuses on fine-tuning models using a batch selection strategy . |
| Approach: | They propose a data selection methodology for fine-tuning machine translation systems that leverages the synergy between a learner model and a pre-trained reference model to enhance overall training effectiveness. |
| Outcome: | The proposed method improves training efficiency by up to fivefold compared to baseline methods. |
Triples-to-isiXhosa (T2X): Addressing the Challenges of Low-Resource Agglutinative Data-to-Text Generation (2024.lrec-main)
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| Challenge: | Existing data-to-text models are designed for the linguistic typology of English, but they are not suitable for low-resource languages. |
| Approach: | They propose a new dataset based on a subset of WebNLG that is agglutinative and low-resource data-to-text. |
| Outcome: | The proposed model outperforms existing models for isiXhosa and Finnish and fine-tunes machine translation models as the best method overall. |