Zhi-Hong Deng

Also published as: Zhihong Deng


Unsupervised Neural Word Segmentation for Chinese via Segmental Language Modeling
Zhiqing Sun | Zhi-Hong Deng
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Previous traditional approaches to unsupervised Chinese word segmentation (CWS) can be roughly classified into discriminative and generative models. The former uses the carefully designed goodness measures for candidate segmentation, while the latter focuses on finding the optimal segmentation of the highest generative probability. However, while there exists a trivial way to extend the discriminative models into neural version by using neural language models, those of generative ones are non-trivial. In this paper, we propose the segmental language models (SLMs) for CWS. Our approach explicitly focuses on the segmental nature of Chinese, as well as preserves several properties of language models. In SLMs, a context encoder encodes the previous context and a segment decoder generates each segment incrementally. As far as we know, we are the first to propose a neural model for unsupervised CWS and achieve competitive performance to the state-of-the-art statistical models on four different datasets from SIGHAN 2005 bakeoff.

MEMD: A Diversity-Promoting Learning Framework for Short-Text Conversation
Meng Zou | Xihan Li | Haokun Liu | Zhihong Deng
Proceedings of the 27th International Conference on Computational Linguistics

Neural encoder-decoder models have been widely applied to conversational response generation, which is a research hot spot in recent years. However, conventional neural encoder-decoder models tend to generate commonplace responses like “I don’t know” regardless of what the input is. In this paper, we analyze this problem from a new perspective: latent vectors. Based on it, we propose an easy-to-extend learning framework named MEMD (Multi-Encoder to Multi-Decoder), in which an auxiliary encoder and an auxiliary decoder are introduced to provide necessary training guidance without resorting to extra data or complicating network’s inner structure. Experimental results demonstrate that our method effectively improve the quality of generated responses according to automatic metrics and human evaluations, yielding more diverse and smooth replies.


Inter-Weighted Alignment Network for Sentence Pair Modeling
Gehui Shen | Yunlun Yang | Zhi-Hong Deng
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Sentence pair modeling is a crucial problem in the field of natural language processing. In this paper, we propose a model to measure the similarity of a sentence pair focusing on the interaction information. We utilize the word level similarity matrix to discover fine-grained alignment of two sentences. It should be emphasized that each word in a sentence has a different importance from the perspective of semantic composition, so we exploit two novel and efficient strategies to explicitly calculate a weight for each word. Although the proposed model only use a sequential LSTM for sentence modeling without any external resource such as syntactic parser tree and additional lexicon features, experimental results show that our model achieves state-of-the-art performance on three datasets of two tasks.


An Unsupervised Multi-Document Summarization Framework Based on Neural Document Model
Shulei Ma | Zhi-Hong Deng | Yunlun Yang
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

In the age of information exploding, multi-document summarization is attracting particular attention for the ability to help people get the main ideas in a short time. Traditional extractive methods simply treat the document set as a group of sentences while ignoring the global semantics of the documents. Meanwhile, neural document model is effective on representing the semantic content of documents in low-dimensional vectors. In this paper, we propose a document-level reconstruction framework named DocRebuild, which reconstructs the documents with summary sentences through a neural document model and selects summary sentences to minimize the reconstruction error. We also apply two strategies, sentence filtering and beamsearch, to improve the performance of our method. Experimental results on the benchmark datasets DUC 2006 and DUC 2007 show that DocRebuild is effective and outperforms the state-of-the-art unsupervised algorithms.

A Position Encoding Convolutional Neural Network Based on Dependency Tree for Relation Classification
Yunlun Yang | Yunhai Tong | Shulei Ma | Zhi-Hong Deng
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing


JEAM: A Novel Model for Cross-Domain Sentiment Classification Based on Emotion Analysis
Kun-Hu Luo | Zhi-Hong Deng | Hongliang Yu | Liang-Chen Wei
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing


A Novel Content Enriching Model for Microblog Using News Corpus
Yunlun Yang | Zhihong Deng | Hongliang Yu
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)


Identifying Sentiment Words Using an Optimization-based Model without Seed Words
Hongliang Yu | Zhi-Hong Deng | Shiyingxue Li
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)