Xinnian Liang


2021

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Unsupervised Keyphrase Extraction by Jointly Modeling Local and Global Context
Xinnian Liang | Shuangzhi Wu | Mu Li | Zhoujun Li
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Embedding based methods are widely used for unsupervised keyphrase extraction (UKE) tasks. Generally, these methods simply calculate similarities between phrase embeddings and document embedding, which is insufficient to capture different context for a more effective UKE model. In this paper, we propose a novel method for UKE, where local and global contexts are jointly modeled. From a global view, we calculate the similarity between a certain phrase and the whole document in the vector space as transitional embedding based models do. In terms of the local view, we first build a graph structure based on the document where phrases are regarded as vertices and the edges are similarities between vertices. Then, we proposed a new centrality computation method to capture local salient information based on the graph structure. Finally, we further combine the modeling of global and local context for ranking. We evaluate our models on three public benchmarks (Inspec, DUC 2001, SemEval 2010) and compare with existing state-of-the-art models. The results show that our model outperforms most models while generalizing better on input documents with different domains and length. Additional ablation study shows that both the local and global information is crucial for unsupervised keyphrase extraction tasks.

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Improving Unsupervised Extractive Summarization with Facet-Aware Modeling
Xinnian Liang | Shuangzhi Wu | Mu Li | Zhoujun Li
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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StyleDGPT: Stylized Response Generation with Pre-trained Language Models
Ze Yang | Wei Wu | Can Xu | Xinnian Liang | Jiaqi Bai | Liran Wang | Wei Wang | Zhoujun Li
Findings of the Association for Computational Linguistics: EMNLP 2020

Generating responses following a desired style has great potentials to extend applications of open-domain dialogue systems, yet is refrained by lacking of parallel data for training. In this work, we explore the challenging task with pre-trained language models that have brought breakthrough to various natural language tasks. To this end, we introduce a KL loss and a style classifier to the fine-tuning step in order to steer response generation towards the target style in both a word-level and a sentence-level. Comprehensive empirical studies with two public datasets indicate that our model can significantly outperform state-of-the-art methods in terms of both style consistency and contextual coherence.