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Huanbo Luan

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13 papers

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NeurIPS Conference 2020 Conference Paper

Graph Random Neural Networks for Semi-Supervised Learning on Graphs

Wenzheng Feng
Jie Zhang
Yuxiao Dong
Yu Han
Huanbo Luan
Qian Xu
Qiang Yang
Evgeny Kharlamov

We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored. However, most existing GNNs inherently suffer from the limitations of over-smoothing, non-robustness, and weak-generalization when labeled nodes are scarce. In this paper, we propose a simple yet effective framework—GRAPH RANDOM NEURAL NETWORKS (GRAND)—to address these issues. In GRAND, we first design a random propagation strategy to perform graph data augmentation. Then we leverage consistency regularization to optimize the prediction consistency of unlabeled nodes across different data augmentations. Extensive experiments on graph benchmark datasets suggest that GRAND significantly outperforms state-of- the-art GNN baselines on semi-supervised node classification. Finally, we show that GRAND mitigates the issues of over-smoothing and non-robustness, exhibiting better generalization behavior than existing GNNs. The source code of GRAND is publicly available at https: //github. com/Grand20/grand.

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IJCAI Conference 2020 Conference Paper

Modeling Voting for System Combination in Machine Translation

Xuancheng Huang
Jiacheng Zhang
Zhixing Tan
Derek F. Wong
Huanbo Luan
Jingfang Xu
Maosong Sun
Yang Liu

System combination is an important technique for combining the hypotheses of different machine translation systems to improve translation performance. Although early statistical approaches to system combination have been proven effective in analyzing the consensus between hypotheses, they suffer from the error propagation problem due to the use of pipelines. While this problem has been alleviated by end-to-end training of multi-source sequence-to-sequence models recently, these neural models do not explicitly analyze the relations between hypotheses and fail to capture their agreement because the attention to a word in a hypothesis is calculated independently, ignoring the fact that the word might occur in multiple hypotheses. In this work, we propose an approach to modeling voting for system combination in machine translation. The basic idea is to enable words in hypotheses from different systems to vote on words that are representative and should get involved in the generation process. This can be done by quantifying the influence of each voter and its preference for each candidate. Our approach combines the advantages of statistical and neural methods since it can not only analyze the relations between hypotheses but also allow for end-to-end training. Experiments show that our approach is capable of better taking advantage of the consensus between hypotheses and achieves significant improvements over state-of-the-art baselines on Chinese-English and English-German machine translation tasks.

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IJCAI Conference 2018 Conference Paper

Cross-Domain Depression Detection via Harvesting Social Media

Tiancheng Shen
Jia Jia
Guangyao Shen
Fuli Feng
Xiangnan He
Huanbo Luan
Jie Tang
Thanassis Tiropanis

Depression detection is a significant issue for human well-being. In previous studies, online detection has proven effective in Twitter, enabling proactive care for depressed users. Owing to cultural differences, replicating the method to other social media platforms, such as Chinese Weibo, however, might lead to poor performance because of insufficient available labeled (self-reported depression) data for model training. In this paper, we study an interesting but challenging problem of enhancing detection in a certain target domain (e. g. Weibo) with ample Twitter data as the source domain. We first systematically analyze the depression-related feature patterns across domains and summarize two major detection challenges, namely isomerism and divergency. We further propose a cross-domain Deep Neural Network model with Feature Adaptive Transformation & Combination strategy (DNN-FATC) that transfers the relevant information across heterogeneous domains. Experiments demonstrate improved performance compared to existing heterogeneous transfer methods or training directly in the target domain (over 3. 4% improvement in F1), indicating the potential of our model to enable depression detection via social media for more countries with different cultural settings.

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AAAI Conference 2017 Conference Paper

Bilingual Lexicon Induction from Non-Parallel Data with Minimal Supervision

Meng Zhang
Haoruo Peng
Yang Liu
Huanbo Luan
Maosong Sun

Building bilingual lexica from non-parallel data is a longstanding natural language processing research problem that could beneﬁt thousands of resource-scarce languages which lack parallel data. Recent advances of continuous word representations have opened up new possibilities for this task, e. g. by establishing cross-lingual mapping between word embeddings via a seed lexicon. The method is however unreliable when there are only a limited number of seeds, which is a reasonable setting for resource-scarce languages. We tackle the limitation by introducing a novel matching mechanism into bilingual word representation learning. It captures extra translation pairs exposed by the seeds to incrementally improve the bilingual word embeddings. In our experiments, we ﬁnd the matching mechanism to substantially improve the quality of the bilingual vector space, which in turn allows us to induce better bilingual lexica with seeds as few as 10.

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IJCAI Conference 2017 Conference Paper

Image-embodied Knowledge Representation Learning

Ruobing Xie
Zhiyuan Liu
Huanbo Luan
Maosong Sun

Entity images could provide significant visual information for knowledge representation learning. Most conventional methods learn knowledge representations merely from structured triples, ignoring rich visual information extracted from entity images. In this paper, we propose a novel Image-embodied Knowledge Representation Learning model (IKRL), where knowledge representations are learned with both triple facts and images. More specifically, we first construct representations for all images of an entity with a neural image encoder. These image representations are then integrated into an aggregated image-based representation via an attention-based method. We evaluate our IKRL models on knowledge graph completion and triple classification. Experimental results demonstrate that our models outperform all baselines on both tasks, which indicates the significance of visual information for knowledge representations and the capability of our models in learning knowledge representations with images.

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TIST Journal 2017 Journal Article

PRISM

Cunchao Tu
Zhiyuan Liu
Huanbo Luan
Maosong Sun

Profession is an important social attribute of people. It plays a crucial role in commercial services such as personalized recommendation and targeted advertising. In practice, profession information is usually unavailable due to privacy and other reasons. In this article, we explore the task of identifying user professions according to their behaviors in social media. The task confronts the following challenges that make it non-trivial: how to incorporate heterogeneous information of user behaviors, how to effectively utilize both labeled and unlabeled data, and how to exploit community structure. To address these challenges, we present a framework called Profession Identification in Social Media. It takes advantage of both personal information and community structure of users in the following aspects: (1) We present a cascaded two-level classifier with heterogeneous personal features to measure the confidence of users belonging to different professions. (2) We present a multi-training process to take advantages of both labeled and unlabeled data to enhance classification performance. (3) We design a profession identification method synthetically considering the confidences from personal features and community structure. We collect a real-world dataset to conduct experiments, and experimental results demonstrate the significant effectiveness of our method compared with other baseline methods. By applying prediction on large-scale users, we also analyze characteristics of microblog users, finding that there are significant diversities among users of different professions in demographics, social network structures, and linguistic styles.

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AAAI Conference 2016 Conference Paper

Building Earth Mover’s Distance on Bilingual Word Embeddings for Machine Translation

Meng Zhang
Yang Liu
Huanbo Luan
Maosong Sun
Tatsuya Izuha
Jie Hao

Following their monolingual counterparts, bilingual word embeddings are also on the rise. As a major application task, word translation has been relying on the nearest neighbor to connect embeddings cross-lingually. However, the nearest neighbor strategy suffers from its inherently local nature and fails to cope with variations in realistic bilingual word embeddings. Furthermore, it lacks a mechanism to deal with manyto-many mappings that often show up across languages. We introduce Earth Mover’s Distance to this task by providing a natural formulation that translates words in a holistic fashion, addressing the limitations of the nearest neighbor. We further extend the formulation to a new task of identifying parallel sentences, which is useful for statistical machine translation systems, thereby expanding the application realm of bilingual word embeddings. We show encouraging performance on both tasks.

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AAAI Conference 2016 Conference Paper

Discrete Image Hashing Using Large Weakly Annotated Photo Collections

Hanwang Zhang
Na Zhao
Xindi Shang
Huanbo Luan
Tat-Seng Chua

We address the problem of image hashing by learning binary codes from large and weakly supervised photo collections. Due to the explosive growth of usergenerated media on the Web, this problem is becoming critical for large-scale visual applications like image retrieval. While most existing hashing methods fail to address this challenge well, our method shows promising improvement due to the following two key advantages. First, we formulate a novel hashing objective that can effectively mine implicit weak supervision by collaborative ﬁltering. Second, we propose a discrete hashing algorithm, offered with efﬁcient optimization, to overcome the inferior optimizations in obtaining binary codes from real-valued solutions. In this way, our method can be considered as a weakly-supervised discrete hashing framework which jointly learns image semantics and their corresponding binary codes. Through training on one million weakly annotated images, our experimental results demonstrate that image retrieval using the proposed hashing method outperforms the other state-of-the-art ones on image and video benchmarks.

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AAAI Conference 2016 Conference Paper

Learning to Appreciate the Aesthetic Effects of Clothing

Jia Jia
Jie Huang
Guangyao Shen
Tao He
Zhiyuan Liu
Huanbo Luan
Chao Yan

How do people describe clothing? The words like “formal” or “casual” are usually used. However, recent works often focus on recognizing or extracting visual features (e. g. , sleeve length, color distribution and clothing pattern) from clothing images accurately. How can we bridge the gap between the visual features and the aesthetic words? In this paper, we formulate this task to a novel three-level framework: visual features (VF) image-scale space (ISS) - aesthetic words space (AWS). Leveraging the art-ﬁeld image-scale space served as an intermediate layer, we ﬁrst propose a Stacked Denoising Autoencoder Guided by Correlative Labels (SDAE- GCL) to map the visual features to the image-scale space; and then according to the semantic distances computed by WordNet: :Similarity, we map the most often used aesthetic words in online clothing shops to the image-scale space too. Employing upper-body menswear images downloaded from several global online clothing shops as experimental data, the results indicate that the proposed three-level framework can help to capture the subtle relationship between visual features and aesthetic words better compared to several baselines. To demonstrate that our three-level framework and its implementation methods are universally applicable, we ﬁnally present some interesting analyses on the fashion trend of menswear in the last 10 years.

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AAAI Conference 2016 Conference Paper

Moodee: An Intelligent Mobile Companion for Sensing Your Stress from Your Social Media Postings

Huijie Lin
Jia Jia
Jie Huang
Enze Zhou
Jingtian Fu
Yejun Liu
Huanbo Luan

In this demo, we build a practical mobile application, Moodee, to help detect and release users’ psychological stress by leveraging users’ social media data in online social networks, and provide an interactive user interface to present users’ and friends’ psychological stress states in an visualized and intuitional way. Given users’ online social media data as input, Moodee intelligently and automatically detects users’ stress states. Moreover, Moodee would recommend users with different links to help release their stress. The main technology of this demo is a novel hybrid model - a factor graph model combined with Deep Neural Network, which can leverage social media content and social interaction information for stress detection. We think that Moodee can be helpful to people’s mental health, which is a vital problem in modern world.

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AAAI Conference 2016 Conference Paper

Representation Learning of Knowledge Graphs with Entity Descriptions

Ruobing Xie
Zhiyuan Liu
Jia Jia
Huanbo Luan
Maosong Sun

Representation learning (RL) of knowledge graphs aims to project both entities and relations into a continuous lowdimensional space. Most methods concentrate on learning representations with knowledge triples indicating relations between entities. In fact, in most knowledge graphs there are usually concise descriptions for entities, which cannot be well utilized by existing methods. In this paper, we propose a novel RL method for knowledge graphs taking advantages of entity descriptions. More speciﬁcally, we explore two encoders, including continuous bag-of-words and deep convolutional neural models to encode semantics of entity descriptions. We further learn knowledge representations with both triples and descriptions. We evaluate our method on two tasks, including knowledge graph completion and entity classiﬁcation. Experimental results on real-world datasets show that, our method outperforms other baselines on the two tasks, especially under the zero-shot setting, which indicates that our method is capable of building representations for novel entities according to their descriptions. The source code of this paper can be obtained from https: //github. com/xrb92/DKRL.

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IJCAI Conference 2015 Conference Paper

Iterative Learning of Parallel Lexicons and Phrases from Non-Parallel Corpora

Meiping Dong
Yang Liu
Huanbo Luan
Maosong Sun
Tatsuya Izuha
Dakun Zhang

While parallel corpora are an indispensable resource for data-driven multilingual natural language processing tasks such as machine translation, they are limited in quantity, quality and coverage. As a result, learning translation models from nonparallel corpora has become increasingly important nowadays, especially for low-resource languages. In this work, we propose a joint model for iteratively learning parallel lexicons and phrases from non-parallel corpora. The model is trained using a Viterbi EM algorithm that alternates between constructing parallel phrases using lexicons and updating lexicons based on the constructed parallel phrases. Experiments on Chinese-English datasets show that our approach learns better parallel lexicons and phrases and improves translation performance significantly.

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IJCAI Conference 2015 Conference Paper

Joint Learning of Character and Word Embeddings

Xinxiong Chen
Lei Xu
Zhiyuan Liu
Maosong Sun
Huanbo Luan

Most word embedding methods take a word as a basic unit and learn embeddings according to words’ external contexts, ignoring the internal structures of words. However, in some languages such as Chinese, a word is usually composed of several characters and contains rich internal information. The semantic meaning of a word is also related to the meanings of its composing characters. Hence, we take Chinese for example, and present a characterenhanced word embedding model (CWE). In order to address the issues of character ambiguity and non-compositional words, we propose multipleprototype character embeddings and an effective word selection method. We evaluate the effectiveness of CWE on word relatedness computation and analogical reasoning. The results show that CWE outperforms other baseline methods which ignore internal character information. The codes and data can be accessed from https: //github. com/ Leonard-Xu/CWE.

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