Yafeng Ren


2021

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Better Combine Them Together! Integrating Syntactic Constituency and Dependency Representations for Semantic Role Labeling
Hao Fei | Shengqiong Wu | Yafeng Ren | Fei Li | Donghong Ji
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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MRN: A Locally and Globally Mention-Based Reasoning Network for Document-Level Relation Extraction
Jingye Li | Kang Xu | Fei Li | Hao Fei | Yafeng Ren | Donghong Ji
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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Improving Text Understanding via Deep Syntax-Semantics Communication
Hao Fei | Yafeng Ren | Donghong Ji
Findings of the Association for Computational Linguistics: EMNLP 2020

Recent studies show that integrating syntactic tree models with sequential semantic models can bring improved task performance, while these methods mostly employ shallow integration of syntax and semantics. In this paper, we propose a deep neural communication model between syntax and semantics to improve the performance of text understanding. Local communication is performed between syntactic tree encoder and sequential semantic encoder for mutual learning of information exchange. Global communication can further ensure comprehensive information propagation. Results on multiple syntax-dependent tasks show that our model outperforms strong baselines by a large margin. In-depth analysis indicates that our method is highly effective in composing sentence semantics.

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Mimic and Conquer: Heterogeneous Tree Structure Distillation for Syntactic NLP
Hao Fei | Yafeng Ren | Donghong Ji
Findings of the Association for Computational Linguistics: EMNLP 2020

Syntax has been shown useful for various NLP tasks, while existing work mostly encodes singleton syntactic tree using one hierarchical neural network. In this paper, we investigate a simple and effective method, Knowledge Distillation, to integrate heterogeneous structure knowledge into a unified sequential LSTM encoder. Experimental results on four typical syntax-dependent tasks show that our method outperforms tree encoders by effectively integrating rich heterogeneous structure syntax, meanwhile reducing error propagation, and also outperforms ensemble methods, in terms of both the efficiency and accuracy.

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Retrofitting Structure-aware Transformer Language Model for End Tasks
Hao Fei | Yafeng Ren | Donghong Ji
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We consider retrofitting structure-aware Transformer language model for facilitating end tasks by proposing to exploit syntactic distance to encode both the phrasal constituency and dependency connection into the language model. A middle-layer structural learning strategy is leveraged for structure integration, accomplished with main semantic task training under multi-task learning scheme. Experimental results show that the retrofitted structure-aware Transformer language model achieves improved perplexity, meanwhile inducing accurate syntactic phrases. By performing structure-aware fine-tuning, our model achieves significant improvements for both semantic- and syntactic-dependent tasks.

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High-order Refining for End-to-end Chinese Semantic Role Labeling
Hao Fei | Yafeng Ren | Donghong Ji
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing

Current end-to-end semantic role labeling is mostly accomplished via graph-based neural models. However, these all are first-order models, where each decision for detecting any predicate-argument pair is made in isolation with local features. In this paper, we present a high-order refining mechanism to perform interaction between all predicate-argument pairs. Based on the baseline graph model, our high-order refining module learns higher-order features between all candidate pairs via attention calculation, which are later used to update the original token representations. After several iterations of refinement, the underlying token representations can be enriched with globally interacted features. Our high-order model achieves state-of-the-art results on Chinese SRL data, including CoNLL09 and Universal Proposition Bank, meanwhile relieving the long-range dependency issues.

2016

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Deceptive Opinion Spam Detection Using Neural Network
Yafeng Ren | Yue Zhang
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Deceptive opinion spam detection has attracted significant attention from both business and research communities. Existing approaches are based on manual discrete features, which can capture linguistic and psychological cues. However, such features fail to encode the semantic meaning of a document from the discourse perspective, which limits the performance. In this paper, we empirically explore a neural network model to learn document-level representation for detecting deceptive opinion spam. In particular, given a document, the model learns sentence representations with a convolutional neural network, which are combined using a gated recurrent neural network with attention mechanism to model discourse information and yield a document vector. Finally, the document representation is used directly as features to identify deceptive opinion spam. Experimental results on three domains (Hotel, Restaurant, and Doctor) show that our proposed method outperforms state-of-the-art methods.

2015

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A Transition-based Model for Joint Segmentation, POS-tagging and Normalization
Tao Qian | Yue Zhang | Meishan Zhang | Yafeng Ren | Donghong Ji
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

2014

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Positive Unlabeled Learning for Deceptive Reviews Detection
Yafeng Ren | Donghong Ji | Hongbin Zhang
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)