2404.02835
arxiv.org · 144 words · saved by 1 readers
N/A
Retrieving Examples from Memory for Retrieval Augmented Neural Machine Translation: A Systematic Comparison Retrieval-Augmented Neural Machine Translation (RAMT) architectures retrieve examples from memory to guide the generation process. While most works in this trend explore new ways to exploit the retrieved examples, the upstream retrieval step is mostly unexplored. In this paper, we study the effect of varying retrieval methods for several translation architectures, to better understand the interplay between these two processes. We conduct experiments in two language pairs in a multi-doma
saved by
related reading
- 2025.findings-emnlp.313.pdfaclanthology.org
- 2025.acl-srw.17.pdfaclanthology.org
- 2024.emnlp-main.914.pdfaclanthology.org
- Exploring In-context Example Generation for Machine Translationaclanthology.org
- 2025.acl-long.429.pdfaclanthology.org
- 2025.acl-long.896.pdfaclanthology.org
- Multi-perspective Alignment for Increasing Naturalness in Neural Machine Translation - ACL Anthologyaclanthology.org
- transformer_attention.pdfarxiv.org
- [2005.11401] Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasksarxiv.org
- [1609.08144] Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translationarxiv-vanity.com
- [2005.14165] Language Models are Few-Shot Learnersarxiv.org
- 1706.03762arxiv.org