The main limiting factor in the development of robust multilingual dialogue
evaluation metrics is the lack of multilingual data and the limited
availability of open sourced multilingual dialogue systems. In this work, we
propose a workaround for this lack of data by leveraging a strong multilingual
pretrained LLM and augmenting existing English dialogue data using Machine
Translation. We empirically show that the naive approach of finetuning a
pretrained multilingual encoder model with translated data is insufficient to
outperform the strong baseline of finetuning a multilingual model with only
source data. Instead, the best approach consists in the careful curation of
translated data using MT Quality Estimation metrics, excluding low quality
translations that hinder its performance.

通过使用机器翻译来增强现有的英文对话数据，我们提出了一个绕过数据缺乏问题的方法，用于开发鲁棒的多语言对话评估指标，并通过实验证明，与仅使用源数据微调多语言模型的强基线相比，仅仅使用翻译数据微调预训练的多语言编码器模型的天真方法无法取得更好的性能。相反，最佳方法是通过使用机器翻译质量估计度量仔细策划翻译数据，排除低质量翻译对其性能的影响。

多语言自动对话评估

Towards Multilingual Automatic Dialogue Evaluation

Our winning entry for the CVPR 2023 Generic Event Boundary Captioning (GEBC)
competition is detailed in this paper. Unlike conventional video captioning
tasks, GEBC demands that the captioning model possess an understanding of
immediate changes in status around the designated video boundary, making it a
difficult task. This paper proposes an effective model LLMVA-GEBC (Large
Language Model with Video Adapter for Generic Event Boundary Captioning): (1)
We utilize a pretrained LLM for generating human-like captions with high
quality. (2) To adapt the model to the GEBC task, we take the video Q-former as
an adapter and train it with the frozen visual feature extractors and LLM. Our
proposed method achieved a 76.14 score on the test set and won the first place
in the challenge. Our code is available at
this https URL .

本文详细介绍了我们在 CVPR 2023 通用事件边界字幕（GEBC）比赛中的得奖作品，提出了 LLMVA-GEBC 模型，该模型采用预训练 LLM 生成高质量的人类式字幕，在训练固定的视觉特征提取和 LLM 同时采用视频 Q-former 作为适配器以适应 GEBC 任务，最终在测试集上取得了 76.14 分的高得分并获得了第一名 。