Multimodal Cross-Lingual Summarization for Videos: A Revisit in Knowledge Distillation Induced Triple-Stage Training Method

Nayu Liu;Kaiwen Wei;Yong Yang;Jianhua Tao;Xian Sun;Fanglong Yao;Hongfeng Yu;Li Jin;Zhao Lv;Cunhang Fan
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Abstract

Multimodal summarization (MS) for videos aims to generate summaries from multi-source information (e.g., video and text transcript), showing promising progress recently. However, existing works are limited to monolingual scenarios, neglecting non-native viewers' needs to understand videos in other languages. It stimulates us to introduce multimodal cross-lingual summarization for videos (MCLS), which aims to generate cross-lingual summaries from multimodal input of videos. Considering the challenge of high annotation cost and resource constraints in MCLS, we propose a knowledge distillation (KD) induced triple-stage training method to assist MCLS by transferring knowledge from abundant monolingual MS data to those data with insufficient volumes. In the triple-stage training method, a video-guided dual fusion network (VDF) is designed as the backbone network to integrate multimodal and cross-lingual information through diverse fusion strategies in the encoder and decoder; What's more, we propose two cross-lingual knowledge distillation strategies: adaptive pooling distillation and language-adaptive warping distillation (LAWD), designed for encoder-level and vocab-level distillation objects to facilitate effective knowledge transfer across cross-lingual sequences of varying lengths between MS and MCLS models. Specifically, to tackle lingual sequences of varying lengths between MS and MCLS models. Specifically, to tackle the challenge of unequal length of parallel cross-language sequences in KD, LAWD can directly conduct cross-language distillation while keeping the language feature shape unchanged to reduce potential information loss. We meticulously annotated the How2-MCLS dataset based on the How2 dataset to simulate MCLS scenarios. Experimental results show that the proposed method achieves competitive performance compared to strong baselines, and can bring substantial performance improvements to MCLS models by transferring knowledge from the MS model.
多模态跨语言视频摘要:知识蒸馏诱导三阶段训练法的再探讨。
视频多模态摘要(MS)旨在从多源信息(如视频和文本转录)中生成摘要,这一技术近来取得了可喜的进展。然而,现有的工作仅限于单语视频场景,忽视了非母语视频观众在实际应用中理解跨语言视频的需求。这促使我们引入了视频多模态跨语言摘要(MCLS),旨在从视频的多模态输入生成跨语言摘要。考虑到 MCLS 所面临的高注释成本和资源限制的挑战,我们提出了一种知识蒸馏(KD)诱导的三阶段训练方法,通过将丰富的单语 MS 数据中的知识转移到数量不足的数据中来帮助 MCLS。在三阶段训练方法中,我们设计了一个视频引导的双融合网络(VDF)作为骨干网络,通过编码器和解码器的不同融合策略整合多模态和跨语言信息;此外,我们还提出了两种跨语言知识蒸馏策略:自适应池化蒸馏和语言自适应翘曲蒸馏(LAWD)。这些策略针对蒸馏对象(即编码器级和词汇表级 KD)量身定制,以促进 MS 和 MCLS 模型之间不同长度的跨语言序列之间的有效知识转移。具体来说,为了解决 KD 中并行跨语言序列长度不等的难题,我们提出的 LAWD 可以直接进行跨语言蒸馏,同时保持语言特征形状不变,以减少潜在的信息丢失。我们在 How2 数据集的基础上对 How2-MCLS 数据集进行了细致的注释,以模拟 MCLS 场景。实验结果表明,与强大的基线相比,所提出的方法取得了具有竞争力的性能,并能通过转移 MS 模型的知识为 MCLS 模型带来实质性的性能改进。
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