| Challenge: | Existing models for cross-lingual information retrieval are not aware of the vocabulary distribution of the retrieval corpus. |
| Approach: | They propose a multi-task learning approach to train a Neural Machine Translation model with a Relevance-based Auxiliary Task (RAT) for search query translation. |
| Outcome: | The proposed model achieves 16% improvement over a strong baseline on Italian-English query-document dataset. |
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| Challenge: | Neural machine translation requires large amount of parallel training text to learn a reasonable quality translation model. |
| Approach: | They propose a multi-task learning approach that leverages monolingual linguistic resources in the source side of a machine translation task. |
| Outcome: | The proposed approach is effective on three translation tasks: English-to-French, English- to-Farsi, and English-à-Vietnamese. |
Multi-Task Neural Model for Agglutinative Language Translation (2020.acl-srw)
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| Challenge: | Neural machine translation (NMT) has been gaining popularity in high-resource translation tasks, but struggles in low-ressource and morphologically-rich scenarios. |
| Approach: | They propose a multi-task neural model that jointly learns to perform bi-directional translation and agglutinative language stemming. |
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Multilingual Neural Machine Translation with Task-Specific Attention (C18-1)
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| Challenge: | Multilingual machine translation is a task of building a system capable of translating between multiple source and target languages. |
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Addressing Asymmetry in Multilingual Neural Machine Translation with Fuzzy Task Clustering (2022.coling-1)
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| Challenge: | Existing clustering methods cannot handle asymmetric problem in multilingual NMT . existing models cannot handle the asymmetry problem since there are thousands of languages involved . |
| Approach: | They propose a fuzzy task clustering method to address the asymmetric problem in multilingual NMT by using task affinity as the clustering criterion. |
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Multilingual Neural Machine Translation (2020.coling-tutorials)
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| Challenge: | In this tutorial, we will cover the latest advances in NMT to enhance low-resource translation. |
| Approach: | They will cover the latest advances in NMT approaches that leverage multilingualism . they will focus on topics such as language divergence, transfer learning and pivoting . |
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Scheduled Multi-task Learning for Neural Chat Translation (2022.acl-long)
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| Challenge: | Existing methods to improve chat translation focus on modeling bilingual dialogue characteristics . however, there is insufficient data and simple joint training methods . |
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Towards Enhancing Faithfulness for Neural Machine Translation (2020.emnlp-main)
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| Challenge: | Neural machine translation (NMT) has achieved great success due to the ability to generate high-quality sentences. |
| Approach: | They propose a training strategy with a multi-task learning paradigm to build a faithfulness enhanced NMT model. |
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Multi-Domain Neural Machine Translation with Word-Level Domain Context Discrimination (D18-1)
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| Challenge: | Experimental results on Chinese-English and English-French multi-domain translation tasks demonstrate the effectiveness of the proposed model. |
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On Search Strategies for Document-Level Neural Machine Translation (2023.findings-acl)
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| Challenge: | Document-level neural machine translation models produce a more consistent output across a document . however, the exact decoding strategy is often not described and not mentioned at all. |
| Approach: | They propose to use standard automatic metrics and specific linguistic phenomena to compare different decoding schemes. |
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Bridging the Gap between Training and Inference: Multi-Candidate Optimization for Diverse Neural Machine Translation (2022.findings-naacl)
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| Challenge: | Existing diverse NMT models lack translation diversity due to a discrepancy between training and inference . despite the success of diverse NTM, there is still a lack of translation diversity . |
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