Lin Chen


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Youling: an AI-assisted Lyrics Creation System
Rongsheng Zhang | Xiaoxi Mao | Le Li | Lin Jiang | Lin Chen | Zhiwei Hu | Yadong Xi | Changjie Fan | Minlie Huang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

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

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Open-Ended Visual Question Answering by Multi-Modal Domain Adaptation
Yiming Xu | Lin Chen | Zhongwei Cheng | Lixin Duan | Jiebo Luo
Findings of the Association for Computational Linguistics: EMNLP 2020

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.


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Multimodality and Dialogue Act Classification in the RoboHelper Project
Lin Chen | Barbara Di Eugenio
Proceedings of the SIGDIAL 2013 Conference


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Improving Sentence Completion in Dialogues with Multi-Modal Features
Anruo Wang | Barbara Di Eugenio | Lin Chen
Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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Co-reference via Pointing and Haptics in Multi-Modal Dialogues
Lin Chen | Barbara Di Eugenio
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


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Exploring Effective Dialogue Act Sequences in One-on-one Computer Science Tutoring Dialogues
Lin Chen | Barbara Di Eugenio | Davide Fossati | Stellan Ohlsson | David Cosejo
Proceedings of the Sixth Workshop on Innovative Use of NLP for Building Educational Applications

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Improving Pronominal and Deictic Co-Reference Resolution with Multi-Modal Features
Lin Chen | Anruo Wang | Barbara Di Eugenio
Proceedings of the SIGDIAL 2011 Conference


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A Lucene and Maximum Entropy Model Based Hedge Detection System
Lin Chen | Barbara Di Eugenio
Proceedings of the Fourteenth Conference on Computational Natural Language Learning – Shared Task