Feiliang Ren


2021

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A Three-Stage Learning Framework for Low-Resource Knowledge-Grounded Dialogue Generation
Shilei Liu | Xiaofeng Zhao | Bochao Li | Feiliang Ren | Longhui Zhang | Shujuan Yin
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Neural conversation models have shown great potentials towards generating fluent and informative responses by introducing external background knowledge. Nevertheless, it is laborious to construct such knowledge-grounded dialogues, and existing models usually perform poorly when transfer to new domains with limited training samples. Therefore, building a knowledge-grounded dialogue system under the low-resource setting is a still crucial issue. In this paper, we propose a novel three-stage learning framework based on weakly supervised learning which benefits from large scale ungrounded dialogues and unstructured knowledge base. To better cooperate with this framework, we devise a variant of Transformer with decoupled decoder which facilitates the disentangled learning of response generation and knowledge incorporation. Evaluation results on two benchmarks indicate that our approach can outperform other state-of-the-art methods with less training data, and even in zero-resource scenario, our approach still performs well.

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A Novel Global Feature-Oriented Relational Triple Extraction Model based on Table Filling
Feiliang Ren | Longhui Zhang | Shujuan Yin | Xiaofeng Zhao | Shilei Liu | Bochao Li | Yaduo Liu
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Table filling based relational triple extraction methods are attracting growing research interests due to their promising performance and their abilities on extracting triples from complex sentences. However, this kind of methods are far from their full potential because most of them only focus on using local features but ignore the global associations of relations and of token pairs, which increases the possibility of overlooking some important information during triple extraction. To overcome this deficiency, we propose a global feature-oriented triple extraction model that makes full use of the mentioned two kinds of global associations. Specifically, we first generate a table feature for each relation. Then two kinds of global associations are mined from the generated table features. Next, the mined global associations are integrated into the table feature of each relation. This “generate-mine-integrate” process is performed multiple times so that the table feature of each relation is refined step by step. Finally, each relation’s table is filled based on its refined table feature, and all triples linked to this relation are extracted based on its filled table. We evaluate the proposed model on three benchmark datasets. Experimental results show our model is effective and it achieves state-of-the-art results on all of these datasets. The source code of our work is available at: https://github.com/neukg/GRTE.

2020

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Knowledge Graph Embedding with Atrous Convolution and Residual Learning
Feiliang Ren | Juchen Li | Huihui Zhang | Shilei Liu | Bochao Li | Ruicheng Ming | Yujia Bai
Proceedings of the 28th International Conference on Computational Linguistics

Knowledge graph embedding is an important task and it will benefit lots of downstream applications. Currently, deep neural networks based methods achieve state-of-the-art performance. However, most of these existing methods are very complex and need much time for training and inference. To address this issue, we propose a simple but effective atrous convolution based knowledge graph embedding method. Compared with existing state-of-the-art methods, our method has following main characteristics. First, it effectively increases feature interactions by using atrous convolutions. Second, to address the original information forgotten issue and vanishing/exploding gradient issue, it uses the residual learning method. Third, it has simpler structure but much higher parameter efficiency. We evaluate our method on six benchmark datasets with different evaluation metrics. Extensive experiments show that our model is very effective. On these diverse datasets, it achieves better results than the compared state-of-the-art methods on most of evaluation metrics. The source codes of our model could be found at https://github.com/neukg/AcrE.

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LMVE at SemEval-2020 Task 4: Commonsense Validation and Explanation Using Pretraining Language Model
Shilei Liu | Yu Guo | BoChao Li | Feiliang Ren
Proceedings of the Fourteenth Workshop on Semantic Evaluation

This paper introduces our system for commonsense validation and explanation. For Sen-Making task, we use a novel pretraining language model based architecture to pick out one of the two given statements that is againstcommon sense. For Explanation task, we use a hint sentence mechanism to improve the performance greatly. In addition, we propose a subtask level transfer learning to share information between subtasks.

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BERTatDE at SemEval-2020 Task 6: Extracting Term-definition Pairs in Free Text Using Pre-trained Model
Huihui Zhang | Feiliang Ren
Proceedings of the Fourteenth Workshop on Semantic Evaluation

Definition extraction is an important task in Nature Language Processing, and it is used to identify the terms and definitions related to terms. The task contains sentence classification task (i.e., classify whether it contains definition) and sequence labeling task (i.e., find the boundary of terms and definitions). The paper describes our system BERTatDE1 in sentence classification task (subtask 1) and sequence labeling task (subtask 2) in the definition extraction (SemEval-2020 Task 6). We use BERT to solve the multi-domain problems including the uncertainty of term boundary that is, different areas have different ways to definite the domain related terms. We use BERT, BiLSTM and attention in subtask 1 and our best result achieved 79.71% in F1 and the eighteenth place in subtask 1. For the subtask 2, we use BERT, BiLSTM and CRF to sequence labeling, and achieve 40.73% in Macro-averaged F1.

2018

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Neural Relation Classification with Text Descriptions
Feiliang Ren | Di Zhou | Zhihui Liu | Yongcheng Li | Rongsheng Zhao | Yongkang Liu | Xiaobo Liang
Proceedings of the 27th International Conference on Computational Linguistics

Relation classification is an important task in natural language processing fields. State-of-the-art methods usually concentrate on building deep neural networks based classification models on the training data in which the relations of the labeled entity pairs are given. However, these methods usually suffer from the data sparsity issue greatly. On the other hand, we notice that it is very easily to obtain some concise text descriptions for almost all of the entities in a relation classification task. The text descriptions can provide helpful supplementary information for relation classification. But they are ignored by most of existing methods. In this paper, we propose DesRC, a new neural relation classification method which integrates entities’ text descriptions into deep neural networks models. We design a two-level attention mechanism to select the most useful information from the “intra-sentence” aspect and the “cross-sentence” aspect. Besides, the adversarial training method is also used to further improve the classification per-formance. Finally, we evaluate the proposed method on the SemEval 2010 dataset. Extensive experiments show that our method achieves much better experimental results than other state-of-the-art relation classification methods.

2012

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Easy-First Chinese POS Tagging and Dependency Parsing
Ji Ma | Tong Xiao | Jingbo Zhu | Feiliang Ren
Proceedings of COLING 2012

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A Demo for Constructing Domain Ontology from Academic Papers
Feiliang Ren
Proceedings of COLING 2012: Demonstration Papers

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A Practical Chinese-English ON Translation Method Based on ON‘s Distribution Characteristics on the Web
Feiliang Ren
Proceedings of COLING 2012: Demonstration Papers

2009

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Chinese-English Organization Name Translation Based on Correlative Expansion
Feiliang Ren | Muhua Zhu | Huizhen Wang | Jingbo Zhu
Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration (NEWS 2009)

2008

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An Effective Hybrid Machine Learning Approach for Coreference Resolution
Feiliang Ren | Jingbo Zhu
Proceedings of the Sixth SIGHAN Workshop on Chinese Language Processing

2006

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Make Word Sense Disambiguation in EBMT Practical
Feiliang Ren | Tianshun Yao
Proceedings of the 20th Pacific Asia Conference on Language, Information and Computation

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Building Translation Memory System by N-gram
Feiliang Ren | Shaoming Liu
Proceedings of the 20th Pacific Asia Conference on Language, Information and Computation