Papers with many-to-one
Multilingual Gloss-free Sign Language Translation: Towards Building a Sign Language Foundation Model (2025.acl-short)
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| Challenge: | Existing studies focus on translating a single SL into a spoken language (one-to-one SLT) however, multilingual SLT remains unexplored due to language conflicts and alignment difficulties across SLs and spoken languages. |
| Approach: | They propose a multilingual gloss-free model that can be used to translate a single SL into a spoken language and generate a token-level SL identification and spoken text. |
| Outcome: | The proposed model supports 10 SLs and handles one-to-one, many-to-1, and many- to-many SLT tasks. |
A Neural Architecture for Automated ICD Coding (P18-1)
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| Challenge: | Medical coding is time-consuming, expensive, and error prone. |
| Approach: | They propose to use diagnosis descriptions (DDs) of a patient as inputs to select the most relevant ICD codes. |
| Outcome: | The proposed algorithms perform on a clinical dataset with 59K patient visits. |
Breaking Down Multilingual Machine Translation (2022.findings-acl)
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| Challenge: | Multilingual training is an essential ingredient in machine translation systems . but it has different effects in different multilingual settings, such as many-to-one, one-tomany and many- to-many learning . |
| Approach: | They compare multilingual training settings with encoders and decoders initialized by multilingual learning . they find important attention heads for each language pair and compare their correlations during inference . |
| Outcome: | The proposed models outperform the best models for high-resource languages and one-to-many models for low-resourced languages. |
Do Multilingual Neural Machine Translation Models Contain Language Pair Specific Attention Heads? (2021.findings-acl)
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| Challenge: | Recent studies on multilingual representations focus on whether there is an emergence of language-independent representations or whether multilingual models partition their weights among different languages. |
| Approach: | They analyze encoder self-attention and encoder-decoder attention heads in a multilingual neural translation model. |
| Outcome: | The proposed model is based on a multilingual neural translation model with a language-independent representation. |
Three Strategies to Improve One-to-Many Multilingual Translation (D18-1)
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| Challenge: | Existing studies show that one-to-many multilingual translation cannot perform on par with the individually trained models. |
| Approach: | They propose to exploit unique initial states for target languages and language-dependent positional embeddings to create hidden cells of the encoder to achieve comparable or even better performance than individually trained models. |
| Outcome: | The proposed methods achieve comparable or even better performance than the individually trained models. |
Neural Transduction for Multilingual Lexical Translation (2020.coling-main)
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| Challenge: | a method for completing multilingual translation dictionaries is proposed . a 27% relative improvement in whole-word accuracy is achieved when multilingual data is unavailable . |
| Approach: | They propose a method for completing multilingual translation dictionaries using multilingual inputs and multilingual decoding objective. |
| Outcome: | The proposed method can synthesize new word forms in multilingual translation dictionaries . it can perform in settings where correct translations have not been observed in text . |
Distributionally Robust Multilingual Machine Translation (2021.emnlp-main)
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| Challenge: | Multilingual neural machine translation (MNMT) learns to translate multiple language pairs with a single model, but the data imbalance hinders it from performing uniformly across language pairs. |
| Approach: | They propose a distributionally robust optimization objective which minimizes the worst-case expected loss over the set of language pairs. |
| Outcome: | The proposed learning objective outperforms baseline methods on three sets of languages and shows that it is cost-effective and efficient. |
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. |