134 citations · 706 across the 103 of their papers we have counts for
15 papers · 1 filter
Embedding Node Structural Role Identity Using Stress Majorization
Lili Wang, Chenghan Huang, Weicheng Ma +2
Nodes in networks may have one or more functions that determine their role in the system. As opposed to local proximity, which captures the local context of nodes, the role identit…
Graph Embedding via Diffusion-Wavelets-Based Node Feature Distribution Characterization
Lili Wang, Chenghan Huang, Weicheng Ma +2
Recent years have seen a rise in the development of representational learning methods for graph data. Most of these methods, however, focus on node-level representation learning at…
GradTS: A Gradient-Based Automatic Auxiliary Task Selection Method Based on Transformer Networks
Weicheng Ma, Renze Lou, Kai Zhang +2
A key problem in multi-task learning (MTL) research is how to select high-quality auxiliary tasks automatically. This paper presents GradTS, an automatic auxiliary task selection m…
Language Model Augmented Relevance Score
Ruibo Liu, Jason Wei, Soroush Vosoughi
Although automated metrics are commonly used to evaluate NLG systems, they often correlate poorly with human judgements. Newer metrics such as BERTScore have addressed many weaknes…
Contributions of Transformer Attention Heads in Multi- and Cross-lingual Tasks
Weicheng Ma, Kai Zhang, Renze Lou +2
This paper studies the relative importance of attention heads in Transformer-based models to aid their interpretability in cross-lingual and multi-lingual tasks. Prior research has…
Modulating Language Models with Emotions
Ruibo Liu, Jason Wei, Chenyan Jia +1
Generating context-aware language that embodies diverse emotions is an important step towards building empathetic NLP systems. In this paper, we propose a formulation of modulated…
Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path
Lili Wang, Chongyang Gao, Chenghan Huang +3
Networks found in the real-world are numerous and varied. A common type of network is the heterogeneous network, where the nodes (and edges) can be of different types. Accordingly,…
A Survey of Data Augmentation Approaches for NLP
Steven Y. Feng, Varun Gangal, Jason Wei +4
Data augmentation has recently seen increased interest in NLP due to more work in low-resource domains, new tasks, and the popularity of large-scale neural networks that require la…
Mitigating Political Bias in Language Models Through Reinforced Calibration
Ruibo Liu, Chenyan Jia, Jason Wei +3
Current large-scale language models can be politically biased as a result of the data they are trained on, potentially causing serious problems when they are deployed in real-world…
BigGreen at SemEval-2021 Task 1: Lexical Complexity Prediction with Assembly Models
Aadil Islam, Weicheng Ma, Soroush Vosoughi
This paper describes a system submitted by team BigGreen to LCP 2021 for predicting the lexical complexity of English words in a given context. We assemble a feature engineering-ba…
Lone Pine at SemEval-2021 Task 5: Fine-Grained Detection of Hate Speech Using BERToxic
Yakoob Khan, Weicheng Ma, Soroush Vosoughi
This paper describes our approach to the Toxic Spans Detection problem (SemEval-2021 Task 5). We propose BERToxic, a system that fine-tunes a pre-trained BERT model to locate toxic…
Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning
Jason Wei, Chengyu Huang, Soroush Vosoughi +2
Few-shot text classification is a fundamental NLP task in which a model aims to classify text into a large number of categories, given only a few training examples per category. Th…
Feature Selection for Multivariate Time Series via Network Pruning
Kang Gu, Soroush Vosoughi, Temiloluwa Prioleau
In recent years, there has been an ever increasing amount of multivariate time series (MTS) data in various domains, typically generated by a large family of sensors such as wearab…
Text Augmentation in a Multi-Task View
Jason Wei, Chengyu Huang, Shiqi Xu +1
Traditional data augmentation aims to increase the coverage of the input distribution by generating augmented examples that strongly resemble original samples in an online fashion…
Political Depolarization of News Articles Using Attribute-aware Word Embeddings
Ruibo Liu, Lili Wang, Chenyan Jia +1
Political polarization in the US is on the rise. This polarization negatively affects the public sphere by contributing to the creation of ideological echo chambers. In this paper,…