Bo Li

May refer to several people

Other people with similar names: Bo Li (NUS, Google), Bo Li (Vanderbilt, UIUC)


2020

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AutoETER: Automated Entity Type Representation for Knowledge Graph Embedding
Guanglin Niu | Bo Li | Yongfei Zhang | Shiliang Pu | Jingyang Li
Findings of the Association for Computational Linguistics: EMNLP 2020

Recent advances in Knowledge Graph Embedding (KGE) allow for representing entities and relations in continuous vector spaces. Some traditional KGE models leveraging additional type information can improve the representation of entities which however totally rely on the explicit types or neglect the diverse type representations specific to various relations. Besides, none of the existing methods is capable of inferring all the relation patterns of symmetry, inversion and composition as well as the complex properties of 1-N, N-1 and N-N relations, simultaneously. To explore the type information for any KG, we develop a novel KGE framework with Automated Entity TypE Representation (AutoETER), which learns the latent type embedding of each entity by regarding each relation as a translation operation between the types of two entities with a relation-aware projection mechanism. Particularly, our designed automated type representation learning mechanism is a pluggable module which can be easily incorporated with any KGE model. Besides, our approach could model and infer all the relation patterns and complex relations. Experiments on four datasets demonstrate the superior performance of our model compared to state-of-the-art baselines on link prediction tasks, and the visualization of type clustering provides clearly the explanation of type embeddings and verifies the effectiveness of our model.

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Graph Enhanced Dual Attention Network for Document-Level Relation Extraction
Bo Li | Wei Ye | Zhonghao Sheng | Rui Xie | Xiangyu Xi | Shikun Zhang
Proceedings of the 28th International Conference on Computational Linguistics

Document-level relation extraction requires inter-sentence reasoning capabilities to capture local and global contextual information for multiple relational facts. To improve inter-sentence reasoning, we propose to characterize the complex interaction between sentences and potential relation instances via a Graph Enhanced Dual Attention network (GEDA). In GEDA, sentence representation generated by the sentence-to-relation (S2R) attention is refined and synthesized by a Heterogeneous Graph Convolutional Network before being fed into the relation-to-sentence (R2S) attention . We further design a simple yet effective regularizer based on the natural duality of the S2R and R2S attention, whose weights are also supervised by the supporting evidence of relation instances during training. An extensive set of experiments on an existing large-scale dataset show that our model achieve competitive performance, especially for the inter-sentence relation extraction, while the neural predictions can also be interpretable and easily observed.

2019

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Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data
Wei Ye | Bo Li | Rui Xie | Zhonghao Sheng | Long Chen | Shikun Zhang
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

In practical scenario, relation extraction needs to first identify entity pairs that have relation and then assign a correct relation class. However, the number of non-relation entity pairs in context (negative instances) usually far exceeds the others (positive instances), which negatively affects a model’s performance. To mitigate this problem, we propose a multi-task architecture which jointly trains a model to perform relation identification with cross-entropy loss and relation classification with ranking loss. Meanwhile, we observe that a sentence may have multiple entities and relation mentions, and the patterns in which the entities appear in a sentence may contain useful semantic information that can be utilized to distinguish between positive and negative instances. Thus we further incorporate the embeddings of character-wise/word-wise BIO tag from the named entity recognition task into character/word embeddings to enrich the input representation. Experiment results show that our proposed approach can significantly improve the performance of a baseline model with more than 10% absolute increase in F1-score, and outperform the state-of-the-art models on ACE 2005 Chinese and English corpus. Moreover, BIO tag embeddings are particularly effective and can be used to improve other models as well.

2018

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Learning Neural Representation for CLIR with Adversarial Framework
Bo Li | Ping Cheng
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

The existing studies in cross-language information retrieval (CLIR) mostly rely on general text representation models (e.g., vector space model or latent semantic analysis). These models are not optimized for the target retrieval task. In this paper, we follow the success of neural representation in natural language processing (NLP) and develop a novel text representation model based on adversarial learning, which seeks a task-specific embedding space for CLIR. Adversarial learning is implemented as an interplay between the generator process and the discriminator process. In order to adapt adversarial learning to CLIR, we design three constraints to direct representation learning, which are (1) a matching constraint capturing essential characteristics of cross-language ranking, (2) a translation constraint bridging language gaps, and (3) an adversarial constraint forcing both language and media invariant to be reached more efficiently and effectively. Through the joint exploitation of these constraints in an adversarial manner, the underlying cross-language semantics relevant to retrieval tasks are better preserved in the embedding space. Standard CLIR experiments show that our model significantly outperforms state-of-the-art continuous space models and is better than the strong machine translation baseline.

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Joint Learning from Labeled and Unlabeled Data for Information Retrieval
Bo Li | Ping Cheng | Le Jia
Proceedings of the 27th International Conference on Computational Linguistics

Recently, a significant number of studies have focused on neural information retrieval (IR) models. One category of works use unlabeled data to train general word embeddings based on term proximity, which can be integrated into traditional IR models. The other category employs labeled data (e.g. click-through data) to train end-to-end neural IR models consisting of layers for target-specific representation learning. The latter idea accounts better for the IR task and is favored by recent research works, which is the one we will follow in this paper. We hypothesize that general semantics learned from unlabeled data can complement task-specific representation learned from labeled data of limited quality, and that a combination of the two is favorable. To this end, we propose a learning framework which can benefit from both labeled and more abundant unlabeled data for representation learning in the context of IR. Through a joint learning fashion in a single neural framework, the learned representation is optimized to minimize both the supervised loss on query-document matching and the unsupervised loss on text reconstruction. Standard retrieval experiments on TREC collections indicate that the joint learning methodology leads to significant better performance of retrieval over several strong baselines for IR.

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Alibaba Submission for WMT18 Quality Estimation Task
Jiayi Wang | Kai Fan | Bo Li | Fengming Zhou | Boxing Chen | Yangbin Shi | Luo Si
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

The goal of WMT 2018 Shared Task on Translation Quality Estimation is to investigate automatic methods for estimating the quality of machine translation results without reference translations. This paper presents the QE Brain system, which proposes the neural Bilingual Expert model as a feature extractor based on conditional target language model with a bidirectional transformer and then processes the semantic representations of source and the translation output with a Bi-LSTM predictive model for automatic quality estimation. The system has been applied to the sentence-level scoring and ranking tasks as well as the word-level tasks for finding errors for each word in translations. An extensive set of experimental results have shown that our system outperformed the best results in WMT 2017 Quality Estimation tasks and obtained top results in WMT 2018.

2017

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NLPTEA 2017 Shared Task – Chinese Spelling Check
Gabriel Fung | Maxime Debosschere | Dingmin Wang | Bo Li | Jia Zhu | Kam-Fai Wong
Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA 2017)

This paper provides an overview along with our findings of the Chinese Spelling Check shared task at NLPTEA 2017. The goal of this task is to develop a computer-assisted system to automatically diagnose typing errors in traditional Chinese sentences written by students. We defined six types of errors which belong to two categories. Given a sentence, the system should detect where the errors are, and for each detected error determine its type and provide correction suggestions. We designed, constructed, and released a benchmark dataset for this task.

2015

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Dependency parsing for Chinese long sentence: A second-stage main structure parsing method
Bo Li | Yunfei Long | Weiguang Qu
Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters

2011

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Clustering Comparable Corpora For Bilingual Lexicon Extraction
Bo Li | Eric Gaussier | Akiko Aizawa
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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Improving Corpus Comparability for Bilingual Lexicon Extraction from Comparable Corpora
Bo Li | Eric Gaussier
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

2008

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Mining Chinese-English Parallel Corpora from the Web
Bo Li | Juan Liu
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-II

2007

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Mining Parallel Text from the Web based on Sentence Alignment
Bo Li | Juan Liu | Huili Zhu
Proceedings of the 21st Pacific Asia Conference on Language, Information and Computation