Emotion Profiling in LLM-Based Literary Translation: Systematic Shifts Across MT and Post-Editing
Quick Answer
This study analyzes emotional profiles in LLM translations of Atwood's 'Oryx and Crake' and their post-edited versions, revealing that MT systems create distinct emotional fingerprints, which compromise the preservation of the author's voice.
Quick Take
This study analyzes emotional profiles in LLM translations of Atwood's 'Oryx and Crake' and their post-edited versions, revealing that MT systems create distinct emotional fingerprints, which compromise the preservation of the author's voice. Using a multilingual approach, the research highlights significant emotional shifts post-editing compared to human translations.
Key Points
- LLM translations exhibit identifiable emotional profiles compared to human translations.
- Post-editing reshapes emotional content toward human-like norms.
- Distinct emotional fingerprints are observed across different MT systems.
- The study uses a large-scale corpus of contemporary Italian science fiction.
- Limited preservation of author's voice noted in MT translations.
Paper Resources
Article Excerpt
From source RSS / original summaryarXiv:2606. 10113v1 Announce Type: new Abstract: This paper investigates whether LLM translations exhibit identifiable emotional profiles and how post-editing reshapes them toward human-like norms. We compare LLM translations of Margaret Atwood's Oryx and Crake with their post-edited versions and a human translation, using a large-scale corpus of contemporary Italian science-fiction as a baseline.
We examine emotion through lexicon-based and multilingual modeling, conducting a fine-grained analysis of emotional variation across systems. We find that MT systems introduce model-specific and statistically significant emotional fingerprints across translations, leading to a limited preservation of an author's voice.
Reader Mode unavailable (could not extract clean content).
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.CL
See more →Time to REFLECT: Can We Trust LLM Judges for Evidence-based Research Agents?
The REFLECT benchmark reveals that current LLM judges are unreliable, achieving below 55% accuracy in evaluating reasoning and evidence use, highlighting the need for improved evaluation methods for deep research agents.