Zi-Yuan Chen


UHop: An Unrestricted-Hop Relation Extraction Framework for Knowledge-Based Question Answering
Zi-Yuan Chen | Chih-Hung Chang | Yi-Pei Chen | Jijnasa Nayak | Lun-Wei Ku
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)

In relation extraction for knowledge-based question answering, searching from one entity to another entity via a single relation is called “one hop”. In related work, an exhaustive search from all one-hop relations, two-hop relations, and so on to the max-hop relations in the knowledge graph is necessary but expensive. Therefore, the number of hops is generally restricted to two or three. In this paper, we propose UHop, an unrestricted-hop framework which relaxes this restriction by use of a transition-based search framework to replace the relation-chain-based search one. We conduct experiments on conventional 1- and 2-hop questions as well as lengthy questions, including datasets such as WebQSP, PathQuestion, and Grid World. Results show that the proposed framework enables the ability to halt, works well with state-of-the-art models, achieves competitive performance without exhaustive searches, and opens the performance gap for long relation paths.


A Unified RvNN Framework for End-to-End Chinese Discourse Parsing
Lin Chuan-An | Hen-Hsen Huang | Zi-Yuan Chen | Hsin-Hsi Chen
Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations

This paper demonstrates an end-to-end Chinese discourse parser. We propose a unified framework based on recursive neural network (RvNN) to jointly model the subtasks including elementary discourse unit (EDU) segmentation, tree structure construction, center labeling, and sense labeling. Experimental results show our parser achieves the state-of-the-art performance in the Chinese Discourse Treebank (CDTB) dataset. We release the source code with a pre-trained model for the NLP community. To the best of our knowledge, this is the first open source toolkit for Chinese discourse parsing. The standalone toolkit can be integrated into subsequent applications without the need of external resources such as syntactic parser.


NLPSA at IJCNLP-2017 Task 2: Imagine Scenario: Leveraging Supportive Images for Dimensional Sentiment Analysis
Szu-Min Chen | Zi-Yuan Chen | Lun-Wei Ku
Proceedings of the IJCNLP 2017, Shared Tasks

Categorical sentiment classification has drawn much attention in the field of NLP, while less work has been conducted for dimensional sentiment analysis (DSA). Recent works for DSA utilize either word embedding, knowledge base features, or bilingual language resources. In this paper, we propose our model for IJCNLP 2017 Dimensional Sentiment Analysis for Chinese Phrases shared task. Our model incorporates word embedding as well as image features, attempting to simulate human’s imaging behavior toward sentiment analysis. Though the performance is not comparable to others in the end, we conduct several experiments with possible reasons discussed, and analyze the drawbacks of our model.