Experiments in Non-Coherent Post-editing
Market pressure on translation productivity joined with technological innovation is likely to fragment and decontextualise translation jobs even more than is cur-rently the case. Many different translators increasingly work on one document at different places, collaboratively working in the cloud. This paper investigates the effect of decontextualised source texts on behaviour by comparing post-editing of sequentially ordered sentences with shuffled sentences from two different texts. The findings suggest that there is little or no effect of the decontextualised source texts on behaviour.
Code (0)
등록된 구현이 없습니다.
Tasks
Active LearningMachine TranslationTranslationSimilar Papers 제목 키워드 기반
XL-Editor: Post-editing Sentences with XLNet
While neural sequence generation models achieve initial success for many NLP applications, the canonical decoding procedure with left-to-right generation order (i.e., autoregressive) in one-pass can not reflect the true …
SentenceStyle TransferText Style TransferTIGER: Text-Instructed 3D Gaussian Retrieval and Coherent Editing
Editing objects within a scene is a critical functionality required across a broad spectrum of applications in computer vision and graphics. As 3D Gaussian Splatting (3DGS) emerges as a frontier in scene representation, …
3DGSRetrievalOmniRefiner: Reinforcement-Guided Local Diffusion Refinement
Reference-guided image generation has progressed rapidly, yet current diffusion models still struggle to preserve fine-grained visual details when refining a generated image using a reference. This limitation arises beca…
Reinforcement LearningImage GenerationVoxHammer: Training-Free Precise and Coherent 3D Editing in Native 3D Space
3D local editing of specified regions is crucial for game industry and robot interaction. Recent methods typically edit rendered multi-view images and then reconstruct 3D models, but they face challenges in precisely pre…
3D Generation"Flex Tape Can't Fix That": Bias and Misinformation in Edited Language Models
Model editing has emerged as a cost-effective strategy to update knowledge stored in language models. However, model editing can have unintended consequences after edits are applied: information unrelated to the edits ca…
MisinformationModel Editing