ShiLiang Zhang


MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction
Linhan Zhang | Qian Chen | Wen Wang | Chong Deng | ShiLiang Zhang | Bing Li | Wei Wang | Xin Cao
Findings of the Association for Computational Linguistics: ACL 2022

Keyphrase extraction (KPE) automatically extracts phrases in a document that provide a concise summary of the core content, which benefits downstream information retrieval and NLP tasks. Previous state-of-the-art methods select candidate keyphrases based on the similarity between learned representations of the candidates and the document. They suffer performance degradation on long documents due to discrepancy between sequence lengths which causes mismatch between representations of keyphrase candidates and the document. In this work, we propose a novel unsupervised embedding-based KPE approach, Masked Document Embedding Rank (MDERank), to address this problem by leveraging a mask strategy and ranking candidates by the similarity between embeddings of the source document and the masked document. We further develop a KPE-oriented BERT (KPEBERT) model by proposing a novel self-supervised contrastive learning method, which is more compatible to MDERank than vanilla BERT. Comprehensive evaluations on six KPE benchmarks demonstrate that the proposed MDERank outperforms state-of-the-art unsupervised KPE approach by average 1.80 F1@15 improvement. MDERank further benefits from KPEBERT and overall achieves average 3.53 F1@15 improvement over SIFRank.

Speaker Overlap-aware Neural Diarization for Multi-party Meeting Analysis
Zhihao Du | ShiLiang Zhang | Siqi Zheng | Zhi-Jie Yan
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Recently, hybrid systems of clustering and neural diarization models have been successfully applied in multi-party meeting analysis. However, current models always treat overlapped speaker diarization as a multi-label classification problem, where speaker dependency and overlaps are not well considered. To overcome the disadvantages, we reformulate overlapped speaker diarization task as a single-label prediction problem via the proposed power set encoding (PSE). Through this formulation, speaker dependency and overlaps can be explicitly modeled. To fully leverage this formulation, we further propose the speaker overlap-aware neural diarization (SOND) model, which consists of a context-independent (CI) scorer to model global speaker discriminability, a context-dependent scorer (CD) to model local discriminability, and a speaker combining network (SCN) to combine and reassign speaker activities. Experimental results show that using the proposed formulation can outperform the state-of-the-art methods based on target speaker voice activity detection, and the performance can be further improved with SOND, resulting in a 6.30% relative diarization error reduction.


The Fixed-Size Ordinally-Forgetting Encoding Method for Neural Network Language Models
ShiLiang Zhang | Hui Jiang | MingBin Xu | JunFeng Hou | LiRong Dai
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)