“社交媒体事件检测是指在从各类社交媒体的内容中挖掘热点事件。在实际情况中,由于数据稀缺,社交媒体事件检测在低资源的情况下表现较差。现有的方法主要通过跨语言知识迁移等方式来缓解低资源问题,但忽略了数据隐私问题。因此,本文提出了基于联邦知识蒸馏的跨语言社交媒体事件检测框架(FedEvent),旨在将富资源客户端知识蒸馏到低资源客户端。该框架通过结合参数高效微调技术和三组对比损失,实现非英文语义空间到英文语义空间的有效映射,并采用联邦蒸馏策略,保障数据隐私的前提下实现知识的迁移。此外,我们还设计了一套四阶段生命周期机制以适应增量场景。最后,我们在真实数据集上进行实验以证明该框架的有效性。”

Recently, a variety of neural models have been proposed for lyrics generation. However, most previous work completes the generation process in a single pass with little human intervention. We believe that lyrics creation is a creative process with human intelligence centered. AI should play a role as an assistant in the lyrics creation process, where human interactions are crucial for high-quality creation. This paper demonstrates Youling, an AI-assisted lyrics creation system, designed to collaborate with music creators. In the lyrics generation process, Youling supports traditional one pass full-text generation mode as well as an interactive generation mode, which allows users to select the satisfactory sentences from generated candidates conditioned on preceding context. The system also provides a revision module which enables users to revise undesired sentences or words of lyrics repeatedly. Besides, Youling allows users to use multifaceted attributes to control the content and format of generated lyrics. The demo video of the system is available at https://youtu.be/DFeNpHk0pm4.

We study the problem of visual question answering (VQA) in images by exploiting supervised domain adaptation, where there is a large amount of labeled data in the source domain but only limited labeled data in the target domain, with the goal to train a good target model. A straightforward solution is to fine-tune a pre-trained source model by using those limited labeled target data, but it usually cannot work well due to the considerable difference between the data distributions of the source and target domains. Moreover, the availability of multiple modalities (i.e., images, questions and answers) in VQA poses further challenges in modeling the transferability between various modalities. In this paper, we address the above issues by proposing a novel supervised multi-modal domain adaptation method for VQA to learn joint feature embeddings across different domains and modalities. Specifically, we align the data distributions of the source and target domains by considering those modalities both jointly and separately. Extensive experiments on the benchmark VQA 2.0 and VizWiz datasets demonstrate that our proposed method outperforms the existing state-of-the-art baselines for open-ended VQA in this challenging domain adaptation setting.