Liang He


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SEEK: Segmented Embedding of Knowledge Graphs
Wentao Xu | Shun Zheng | Liang He | Bin Shao | Jian Yin | Tie-Yan Liu
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

In recent years, knowledge graph embedding becomes a pretty hot research topic of artificial intelligence and plays increasingly vital roles in various downstream applications, such as recommendation and question answering. However, existing methods for knowledge graph embedding can not make a proper trade-off between the model complexity and the model expressiveness, which makes them still far from satisfactory. To mitigate this problem, we propose a lightweight modeling framework that can achieve highly competitive relational expressiveness without increasing the model complexity. Our framework focuses on the design of scoring functions and highlights two critical characteristics: 1) facilitating sufficient feature interactions; 2) preserving both symmetry and antisymmetry properties of relations. It is noteworthy that owing to the general and elegant design of scoring functions, our framework can incorporate many famous existing methods as special cases. Moreover, extensive experiments on public benchmarks demonstrate the efficiency and effectiveness of our framework. Source codes and data can be found at

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SAS: Dialogue State Tracking via Slot Attention and Slot Information Sharing
Jiaying Hu | Yan Yang | Chencai Chen | Liang He | Zhou Yu
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Dialogue state tracker is responsible for inferring user intentions through dialogue history. Previous methods have difficulties in handling dialogues with long interaction context, due to the excessive information. We propose a Dialogue State Tracker with Slot Attention and Slot Information Sharing (SAS) to reduce redundant information’s interference and improve long dialogue context tracking. Specially, we first apply a Slot Attention to learn a set of slot-specific features from the original dialogue and then integrate them using a slot information sharing module. Our model yields a significantly improved performance compared to previous state-of the-art models on the MultiWOZ dataset.

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ECNU-SenseMaker at SemEval-2020 Task 4: Leveraging Heterogeneous Knowledge Resources for Commonsense Validation and Explanation
Qian Zhao | Siyu Tao | Jie Zhou | Linlin Wang | Xin Lin | Liang He
Proceedings of the Fourteenth Workshop on Semantic Evaluation

This paper describes our system for SemEval-2020 Task 4: Commonsense Validation and Explanation (Wang et al., 2020). We propose a novel Knowledge-enhanced Graph Attention Network (KEGAT) architecture for this task, leveraging heterogeneous knowledge from both the structured knowledge base (i.e. ConceptNet) and unstructured text to better improve the ability of a machine in commonsense understanding. This model has a powerful commonsense inference capability via utilizing suitable commonsense incorporation methods and upgraded data augmentation techniques. Besides, an internal sharing mechanism is cooperated to prohibit our model from insufficient and excessive reasoning for commonsense. As a result, this model performs quite well in both validation and explanation. For instance, it achieves state-of-the-art accuracy in the subtask called Commonsense Explanation (Multi-Choice). We officially name the system as ECNU-SenseMaker. Code is publicly available at

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SentiX: A Sentiment-Aware Pre-Trained Model for Cross-Domain Sentiment Analysis
Jie Zhou | Junfeng Tian | Rui Wang | Yuanbin Wu | Wenming Xiao | Liang He
Proceedings of the 28th International Conference on Computational Linguistics

Pre-trained language models have been widely applied to cross-domain NLP tasks like sentiment analysis, achieving state-of-the-art performance. However, due to the variety of users’ emotional expressions across domains, fine-tuning the pre-trained models on the source domain tends to overfit, leading to inferior results on the target domain. In this paper, we pre-train a sentiment-aware language model (SentiX) via domain-invariant sentiment knowledge from large-scale review datasets, and utilize it for cross-domain sentiment analysis task without fine-tuning. We propose several pre-training tasks based on existing lexicons and annotations at both token and sentence levels, such as emoticons, sentiment words, and ratings, without human interference. A series of experiments are conducted and the results indicate the great advantages of our model. We obtain new state-of-the-art results in all the cross-domain sentiment analysis tasks, and our proposed SentiX can be trained with only 1% samples (18 samples) and it achieves better performance than BERT with 90% samples.


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Exploiting Noisy Data in Distant Supervision Relation Classification
Kaijia Yang | Liang He | Xin-yu Dai | Shujian Huang | Jiajun Chen
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Distant supervision has obtained great progress on relation classification task. However, it still suffers from noisy labeling problem. Different from previous works that underutilize noisy data which inherently characterize the property of classification, in this paper, we propose RCEND, a novel framework to enhance Relation Classification by Exploiting Noisy Data. First, an instance discriminator with reinforcement learning is designed to split the noisy data into correctly labeled data and incorrectly labeled data. Second, we learn a robust relation classifier in semi-supervised learning way, whereby the correctly and incorrectly labeled data are treated as labeled and unlabeled data respectively. The experimental results show that our method outperforms the state-of-the-art models.


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EmojiIt at SemEval-2018 Task 2: An Effective Attention-Based Recurrent Neural Network Model for Emoji Prediction with Characters Gated Words
Shiyun Chen | Maoquan Wang | Liang He
Proceedings of The 12th International Workshop on Semantic Evaluation

This paper presents our single model to Subtask 1 of SemEval 2018 Task 2: Emoji Prediction in English. In order to predict the emoji that may be contained in a tweet, the basic model we use is an attention-based recurrent neural network which has achieved satisfactory performs in Natural Language processing. Considering the text comes from social media, it contains many discrepant abbreviations and online terms, we also combine word-level and character-level word vector embedding to better handling the words not appear in the vocabulary. Our single model1 achieved 29.50% Macro F-score in test data and ranks 9th among 48 teams.


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Chinese Named Entity Recognition with a Multi-Phase Model
Junsheng Zhou | Liang He | Xinyu Dai | Jiajun Chen
Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing