Translation of Multifaceted Data without Re-Training of Machine Translation System
Authors: Hyeonseok Moon, Seungyoon Lee, Seongtae Hong, Seungjun Lee, Chanjun Park, Heuiseok Lim


Reviewer paper summaries: The authors first point out that current work ignore the interrelation between components of data point in translation. Then they introduce a Catalyst Statement to enhance the intra-data relation and Indicator Token to assist the decomposition of  a translated sequence into its respective data components. And this method leads to some performance gain.
Summary of strengths: This paper is easy to follow and clearly illustrate their method.

It achieve reasonable performance gain when comparing to baselines.

It has both automatic metric and LLM evaluation and both show positive results.
Summary of weaknesses: The authors should provide some experimental results about their method's robustness to different indicator tokens.

Could the author provide more samples? I can see there may be some improvement. But it is still mysterious to me if no more samples or explanation provided.

Having LLM scoring for the generation is OK. But win-rate of direct comparison may be better.

Another concern about the evaluation is that authors didn't illustrate if all data components of a data point are evaluated together or independently for the baseline.
Comments, suggestions and typos: The use of "task composition" is misleading. L056-057
Overall assessment scores: 3