Papers with high-quality translation
Quality Estimation without Human-labeled Data (2021.eacl-main)
Copied to clipboard
Yi-Lin Tuan, Ahmed El-Kishky, Adithya Renduchintala, Vishrav Chaudhary, Francisco Guzmán, Lucia Specia
| Challenge: | Quality estimation aims to measure the quality of translated content without access to a reference translation. |
| Approach: | They propose a method that uses synthetic training data to train supervised quality estimation models. |
| Outcome: | The proposed model outperforms models trained on human-annotated data for sentence and word-level prediction. |
Glancing Transformer for Non-Autoregressive Neural Machine Translation (2021.acl-long)
Copied to clipboard
| Challenge: | Existing non-autoregressive neural machine translation methods are either inferior to Transformer or require multiple decoding passes, leading to reduced speedup. |
| Approach: | They propose a Glancing Language Model (GLM) for single-pass parallel generation models and Glancing Transformer (GLAT) with only single- pass decoding, GLAT is able to generate high-quality translation with 8-15 speedup. |
| Outcome: | The proposed model outperforms all previous non-autoregressive methods on multiple language directions and is nearly comparable to Transformer. |
Encoding Gated Translation Memory into Neural Machine Translation (D18-1)
Copied to clipboard
| Challenge: | Neural machine translation (MT) technology has made significant progress in the past few years. |
| Approach: | They propose a method to combine the strengths of TM and neural machine translation (NMT) they use a gating mechanism to balance the impact of the TM match on the NMT decoder . |
| Outcome: | The proposed method improves translation quality by over 10 BLEU points when fuzzy matches are higher than 50% on the UN corpus. |
End-to-End Simultaneous Speech Translation with Differentiable Segmentation (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to perform simultaneous speech translation always separate segmentation from the underlying model. |
| Approach: | They propose to use Differentiable Segmentation (DiSeg) to learn segmentation from the translation model. |
| Outcome: | Experimental results show that the proposed model can learn segmentation from the translation model. |
From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan (2026.acl-long)
Copied to clipboard
Lei Yang, Leiyu Pan, Bojian Xiong, Renren Jin, Shaowei Zhang, Yue Chen, Ling Shi, Jiang Zhou, Junru Wu, Zhen Wang, Jianxiang Peng, Juesi Xiao, Tianyu Dong, Zhuowen Han, Zhuo Chen, Yuqi Ren, Deyi Xiong
| Challenge: | Large language models have achieved remarkable success across a wide range of tasks, yet their performance remains heavily biased toward high-resource languages. |
| Approach: | They propose a pipeline for advancing Tibetan language modeling through multilingual continual pre-training with Tibetan, Chinese, and English. |
| Outcome: | The proposed model outperforms open-source and Tibetan-focused models on diverse tasks. |