Jun Sun


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Layer-wise Fusion with Modality Independence Modeling for Multi-modal Emotion Recognition
Jun Sun | Shoukang Han | Yu-Ping Ruan | Xiaoning Zhang | Shu-Kai Zheng | Yulong Liu | Yuxin Huang | Taihao Li
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Multi-modal emotion recognition has gained increasing attention in recent years due to its widespread applications and the advances in multi-modal learning approaches. However, previous studies primarily focus on developing models that exploit the unification of multiple modalities. In this paper, we propose that maintaining modality independence is beneficial for the model performance. According to this principle, we construct a dataset, and devise a multi-modal transformer model. The new dataset, CHinese Emotion Recognition dataset with Modality-wise Annotions, abbreviated as CHERMA, provides uni-modal labels for each individual modality, and multi-modal labels for all modalities jointly observed. The model consists of uni-modal transformer modules that learn representations for each modality, and a multi-modal transformer module that fuses all modalities. All the modules are supervised by their corresponding labels separately, and the forward information flow is uni-directionally from the uni-modal modules to the multi-modal module. The supervision strategy and the model architecture guarantee each individual modality learns its representation independently, and meanwhile the multi-modal module aggregates all information. Extensive empirical results demonstrate that our proposed scheme outperforms state-of-the-art alternatives, corroborating the importance of modality independence in multi-modal emotion recognition. The dataset and codes are availabel at https://github.com/sunjunaimer/LFMIM


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Automatic Identifying Entity Type in Linked Data
Qingliang Miao | Ruiyu Fang | Shuangyong Song | Zhongguang Zheng | Lu Fang | Yao Meng | Jun Sun
Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Posters


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Feature Reduction Using Ensemble Approach
Yingju Xia | Cuiqin Hou | Zhuoran Xu | Jun Sun
Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters


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Exploring Syntactic Structural Features for Sub-Tree Alignment Using Bilingual Tree Kernels
Jun Sun | Min Zhang | Chew Lim Tan
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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Discriminative Induction of Sub-Tree Alignment using Limited Labeled Data
Jun Sun | Min Zhang | Chew Lim Tan
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)


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A non-contiguous Tree Sequence Alignment-based Model for Statistical Machine Translation
Jun Sun | Min Zhang | Chew Lim Tan
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP


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I2R Chinese-English translation system for IWSLT 2007
Boxing Chen | Jun Sun | Hongfei Jiang | Min Zhang | Ai Ti Aw
Proceedings of the Fourth International Workshop on Spoken Language Translation

In this paper, we describe the system and approach used by Institute for Infocomm Research (I2R) for the IWSLT 2007 spoken language evaluation campaign. A multi-pass approach is exploited to generate and select best translation. First, we use two decoders namely the open source Moses and an in-home syntax-based decoder to generate N-best lists. Next we spawn new translation entries through a word-based n-gram language model estimated on the former N-best entries. Finally, we join the N-best lists from the previous two passes, and select the best translation by rescoring them with additional feature functions. In particular, this paper reports our effort on new translation entry generation and system combination. The performance on development and test sets are reported. The system was ranked first with respect to the BLEU measure in Chinese-to-English open data track.

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A tree-to-tree alignment-based model for statistical machine translation
Min Zhang | Hongfei Jiang | Ai Ti Aw | Jun Sun | Sheng Li | Chew Lim Tan
Proceedings of Machine Translation Summit XI: Papers