Papers with translation scenarios
Neural Machine Translation for Agglutinative Languages via Data Rejuvenation (2025.acl-srw)
Copied to clipboard
| Challenge: | Recent years, advances in Neural Machine Translation (NMT) heavily rely on large-scale parallel corpora. |
| Approach: | They propose to combine fine-grained inactive sample identification with target-side rejuvenation to improve translation quality from agglutinative languages. |
| Outcome: | The proposed framework improves on four low-resource agglutinative language tasks. |
A Compact and Language-Sensitive Multilingual Translation Method (P19-1)
Copied to clipboard
| Challenge: | Existing paradigms for multilingual neural machine translation do not make full use of language commonality and parameter sharing. |
| Approach: | They propose a multilingual neural machine translation paradigm with one encoder-decoder model that makes full use of language commonality and parameter sharing. |
| Outcome: | The proposed method outperforms strong standard multilingual translation systems on WMT and IWSLT datasets. |
Neural Machine Translation with Contrastive Translation Memories (2022.emnlp-main)
Copied to clipboard
| Challenge: | Experimental results show that retrieval-augmented NMT model obtains substantial improvements over strong baselines in the benchmark dataset. |
| Approach: | They propose a retrieval-augmented NMT model that is holistically similar to the source sentence while individually contrastive to each other. |
| Outcome: | The proposed model improves on baselines in the translation task. |
ETHICA-MT: Introducing a Framework and Dataset for Studying Ethical Orientations in LLM-based Machine Translation (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing models for translation have not been systematically examined for their default ethical tendencies or their ability to employ and prioritize specified ethical approaches in conflicted translation situations. |
| Approach: | They propose a framework for examining ethical reasoning and implementation in large language models (LLMs) that systematically examines default ethical tendencies and their ability to employ and prioritize specified ethical approaches in conflicted translation situations. |
| Outcome: | The proposed framework examines the ethical reasoning and implementation of large language models in translation tasks. |