Lanjun Wang


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T3-Vis: visual analytic for Training and fine-Tuning Transformers in NLP
Raymond Li | Wen Xiao | Lanjun Wang | Hyeju Jang | Giuseppe Carenini
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Transformers are the dominant architecture in NLP, but their training and fine-tuning is still very challenging. In this paper, we present the design and implementation of a visual analytic framework for assisting researchers in such process, by providing them with valuable insights about the model’s intrinsic properties and behaviours. Our framework offers an intuitive overview that allows the user to explore different facets of the model (e.g., hidden states, attention) through interactive visualization, and allows a suite of built-in algorithms that compute the importance of model components and different parts of the input sequence. Case studies and feedback from a user focus group indicate that the framework is useful, and suggest several improvements. Our framework is available at:


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A High Precision Pipeline for Financial Knowledge Graph Construction
Sarah Elhammadi | Laks V.S. Lakshmanan | Raymond Ng | Michael Simpson | Baoxing Huai | Zhefeng Wang | Lanjun Wang
Proceedings of the 28th International Conference on Computational Linguistics

Motivated by applications such as question answering, fact checking, and data integration, there is significant interest in constructing knowledge graphs by extracting information from unstructured information sources, particularly text documents. Knowledge graphs have emerged as a standard for structured knowledge representation, whereby entities and their inter-relations are represented and conveniently stored as (subject,predicate,object) triples in a graph that can be used to power various downstream applications. The proliferation of financial news sources reporting on companies, markets, currencies, and stocks presents an opportunity for extracting valuable knowledge about this crucial domain. In this paper, we focus on constructing a knowledge graph automatically by information extraction from a large corpus of financial news articles. For that purpose, we develop a high precision knowledge extraction pipeline tailored for the financial domain. This pipeline combines multiple information extraction techniques with a financial dictionary that we built, all working together to produce over 342,000 compact extractions from over 288,000 financial news articles, with a precision of 78% at the top-100 extractions.The extracted triples are stored in a knowledge graph making them readily available for use in downstream applications.