Rui Li


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To Copy Rather Than Memorize: A Vertical Learning Paradigm for Knowledge Graph Completion
Rui Li | Xu Chen | Chaozhuo Li | Yanming Shen | Jianan Zhao | Yujing Wang | Weihao Han | Hao Sun | Weiwei Deng | Qi Zhang | Xing Xie
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Embedding models have shown great power in knowledge graph completion (KGC) task. By learning structural constraints for each training triple, these methods implicitly memorize intrinsic relation rules to infer missing links. However, this paper points out that the multi-hop relation rules are hard to be reliably memorized due to the inherent deficiencies of such implicit memorization strategy, making embedding models underperform in predicting links between distant entity pairs. To alleviate this problem, we present Vertical Learning Paradigm (VLP), which extends embedding models by allowing to explicitly copy target information from related factual triples for more accurate prediction. Rather than solely relying on the implicit memory, VLP directly provides additional cues to improve the generalization ability of embedding models, especially making the distant link prediction significantly easier. Moreover, we also propose a novel relative distance based negative sampling technique (ReD) for more effective optimization. Experiments demonstrate the validity and generality of our proposals on two standard benchmarks. Our code is available at


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Asymmetric Mutual Learning for Multi-source Unsupervised Sentiment Adaptation with Dynamic Feature Network
Rui Li | Cheng Liu | Dazhi Jiang
Proceedings of the 29th International Conference on Computational Linguistics

Recently, fine-tuning the pre-trained language model (PrLM) on labeled sentiment datasets demonstrates impressive performance. However, collecting labeled sentiment dataset is time-consuming, and fine-tuning the whole PrLM brings about much computation cost. To this end, we focus on multi-source unsupervised sentiment adaptation problem with the pre-trained features, which is more practical and challenging. We first design a dynamic feature network to fully exploit the extracted pre-trained features for efficient domain adaptation. Meanwhile, with the difference of the traditional source-target domain alignment methods, we propose a novel asymmetric mutual learning strategy, which can robustly estimate the pseudo-labels of the target domain with the knowledge from all the other source models. Experiments on multiple sentiment benchmarks show that our method outperforms the recent state-of-the-art approaches, and we also conduct extensive ablation studies to verify the effectiveness of each the proposed module.


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Treasures Outside Contexts: Improving Event Detection via Global Statistics
Rui Li | Wenlin Zhao | Cheng Yang | Sen Su
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Event detection (ED) aims at identifying event instances of specified types in given texts, which has been formalized as a sequence labeling task. As far as we know, existing neural-based ED models make decisions relying entirely on the contextual semantic features of each word in the inputted text, which we find is easy to be confused by the varied contexts in the test stage. To this end, we come up with the idea of introducing a set of statistical features from word-event co-occurrence frequencies in the entire training set to cooperate with contextual features. Specifically, we propose a Semantic and Statistic-Joint Discriminative Network (SS-JDN) consisting of a semantic feature extractor, a statistical feature extractor, and a joint event discriminator. In experiments, SS-JDN effectively exceeds ten recent strong baselines on ACE2005 and KBP2015 datasets. Further, we perform extensive experiments to comprehensively probe SS-JDN.


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Multi-Granularity Chinese Word Embedding
Rongchao Yin | Quan Wang | Peng Li | Rui Li | Bin Wang
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing


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Annotation Schemes to Encode Domain Knowledge in Medical Narratives
Wilson McCoy | Cecilia Ovesdotter Alm | Cara Calvelli | Rui Li | Jeff B. Pelz | Pengcheng Shi | Anne Haake
Proceedings of the Sixth Linguistic Annotation Workshop