Summary of Translation Of Multifaceted Data Without Re-training Of Machine Translation Systems, by Hyeonseok Moon et al.
Translation of Multifaceted Data without Re-Training of Machine Translation Systems
by Hyeonseok Moon, Seungyoon Lee, Seongtae Hong, Seungjun Lee, Chanjun Park, Heuiseok Lim
First submitted to arxiv on: 25 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed MT pipeline aims to improve translation quality by considering intra-data relations between components within a single data point. This is achieved by concatenating all components into a single sequence, translating it, and then reconstructing the original components. The approach utilizes a Catalyst Statement (CS) and Indicator Token (IT) to enhance this process. Experimental results show that the proposed method outperforms the conventional approach in training data quality, leading to improved model performance on web page ranking (WPR) and question generation (QG) tasks by 2.690 and 0.845 points respectively in the XGLUE benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way of translating data for language models. Instead of translating each part separately, they combine all parts into one sequence, translate it, and then break it back down into its original parts. This helps to improve the quality of the translated data, making it better for training models. The approach was tested on two tasks: ranking web pages and generating questions. It outperformed the old way by 2.690 points on the first task and 0.845 points on the second. |
Keywords
» Artificial intelligence » Token » Translation
