Data-Driven Approach for Formality-Sensitive Machine Translation: Language-Specific Handling and Synthetic Data Generation

Seugnjun Lee, Hyeonseok Moon, Chanjun Park, Heuiseok Lim
6/26/2023
cs.CLcs.AI

No confident code implementation found for this paper.

Abstract

In this paper, we introduce a data-driven approach for Formality-Sensitive Machine Translation (FSMT) that caters to the unique linguistic properties of four target languages. Our methodology centers on two core strategies: 1) language-specific data handling, and 2) synthetic data generation using large-scale language models and empirical prompt engineering. This approach demonstrates a considerable improvement over the baseline, highlighting the effectiveness of data-centric techniques. Our prompt engineering strategy further improves performance by producing superior synthetic translation examples.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages

86% match2023

DataArc-SynData-Toolkit: A Unified Closed-Loop Framework for Multi-Path, Multimodal, and Multilingual Data Synthesis

85% match2026

Multi-CrossRE A Multi-Lingual Multi-Domain Dataset for Relation Extraction

85% match1 repo492023

Prompt-Learning for Cross-Lingual Relation Extraction

85% match2023

Exploring Methods for Building Dialects-Mandarin Code-Mixing Corpora: A Case Study in Taiwanese Hokkien

85% match2023

RAMP: Retrieval and Attribute-Marking Enhanced Prompting for Attribute-Controlled Translation

85% match2023

Cite this paper

@article{lee2023datadriven,
  title  = {Data-Driven Approach for Formality-Sensitive Machine Translation: Language-Specific Handling and Synthetic Data Generation},
  author = {Seugnjun Lee and Hyeonseok Moon and Chanjun Park and Heuiseok Lim},
  year   = {2023},
  eprint = {2306.14514},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.14514v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2306.14514.svg)](https://codeofpaper.com/paper/2306.14514)

Discussion