Yunlun Yang


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


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)