Quan Wang

Generalized End-to-End Loss for Speaker Verification

2017 · arXiv (Cornell University)

In this paper, we propose a new loss function called generalized end-to-end (GE2E) loss, which makes the training of speaker verification models more efficient than our previous tuple-based end-to-end (TE2E) loss function. Unlike TE2E, the …

Interpretable and Efficient Heterogeneous Graph Convolutional Network

2021 · IEEE Transactions on Knowledge and Data Engineering

Graph Convolutional Network (GCN) has achieved extraordinary success in learning representations of nodes in graphs. However, regarding Heterogeneous Information Network (HIN), existing HIN-oriented GCN methods still suffer from two deficiencies: (1) they cannot flexibly explore …

Semantically Smooth Knowledge Graph Embedding

2015

Shu Guo, Quan Wang, Bin Wang, Lihong Wang, Li Guo. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long …

Knowledge Graph Embedding: A Survey of Approaches and Applications

2017 · IEEE Transactions on Knowledge and Data Engineering

Knowledge graph (KG) embedding is to embed components of a KG including entities and relations into continuous vector spaces, so as to simplify the manipulation while preserving the inherent structure of the KG. It can …

Curriculum Learning for Natural Language Understanding

2020

With the great success of pre-trained language models, the pretrain-finetune paradigm now becomes the undoubtedly dominant solution for natural language understanding (NLU) tasks. At the fine-tune stage, target task data is usually introduced in a …

Event Extraction as Multi-turn Question Answering

2020

Event extraction, which aims to identify event triggers of pre-defined event types and their arguments of specific roles, is a challenging task in NLP. Most traditional approaches formulate this task as classification problems, with event …

Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction

2021 · Proceedings of the AAAI Conference on Artificial Intelligence

Entities, as the essential elements in relation extraction tasks, exhibit certain structure. In this work, we formulate such entity structure as distinctive dependencies between mention pairs. We then propose SSAN, which incorporates these structural dependencies …