62 citations · 124 across the 17 of their papers we have counts for
17 papers
Joint Latent Topic Discovery and Expectation Modeling for Financial Markets
Lili Wang, Chenghan Huang, Chongyang Gao +2
In the pursuit of accurate and scalable quantitative methods for financial market analysis, the focus has shifted from individual stock models to those capturing interrelations bet…
Graph-Level Embedding for Time-Evolving Graphs
Lili Wang, Chenghan Huang, Weicheng Ma +2
Graph representation learning (also known as network embedding) has been extensively researched with varying levels of granularity, ranging from nodes to graphs. While most prior w…
EnCBP: A New Benchmark Dataset for Finer-Grained Cultural Background Prediction in English
Weicheng Ma, Samiha Datta, Lili Wang +1
While cultural backgrounds have been shown to affect linguistic expressions, existing natural language processing (NLP) research on culture modeling is overly coarse-grained and do…
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…
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…
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,…
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…
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,…
Improvements and Extensions on Metaphor Detection
Weicheng Ma, Ruibo Liu, Lili Wang +1
Metaphors are ubiquitous in human language. The metaphor detection task (MD) aims at detecting and interpreting metaphors from written language, which is crucial in natural languag…
An Empirical Survey of Unsupervised Text Representation Methods on Twitter Data
Lili Wang, Chongyang Gao, Jason Wei +3
The field of NLP has seen unprecedented achievements in recent years. Most notably, with the advent of large-scale pre-trained Transformer-based language models, such as BERT, ther…
Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation
Ruibo Liu, Guangxuan Xu, Chenyan Jia +3
Data augmentation is proven to be effective in many NLU tasks, especially for those suffering from data scarcity. In this paper, we present a powerful and easy to deploy text augme…
Embedding Node Structural Role Identity into Hyperbolic Space
Lili Wang, Ying Lu, Chenghan Huang +1
Recently, there has been an interest in embedding networks in hyperbolic space, since hyperbolic space has been shown to work well in capturing graph/network structure as it can na…
Towards Improved Model Design for Authorship Identification: A Survey on Writing Style Understanding
Weicheng Ma, Ruibo Liu, Lili Wang +1
Authorship identification tasks, which rely heavily on linguistic styles, have always been an important part of Natural Language Understanding (NLU) research. While other tasks bas…
Emoji Prediction: Extensions and Benchmarking
Weicheng Ma, Ruibo Liu, Lili Wang +1
Emojis are a succinct form of language which can express concrete meanings, emotions, and intentions. Emojis also carry signals that can be used to better understand communicative…
Salienteye: Maximizing Engagement While Maintaining Artistic Style on Instagram Using Deep Neural Networks
Lili Wang, Ruibo Liu, Soroush Vosoughi
Instagram has become a great venue for amateur and professional photographers alike to showcase their work. It has, in other words, democratized photography. Generally, photographe…