6 accepted papers
Prediction using the ground truth sounds like an oxymoron in machine learning. However, such an unrealistic setting was used in hundreds, if not thousands of papers in the area of finding graph representations. To evaluate the multi-label problem of node classification by using the obtained represen…
In this demonstration, we develop an interactive tool, HIVE, to demonstrate the ability and versatility of an explainable risk ranking model with a special focus on financial use cases. HIVE is a web-based tool that provides users with automated highlighted financial statements, and HIVE is designed…
While internalized “implicit knowledge” in pretrained transformers has led to fruitful progress in many natural language understanding tasks, how to most effectively elicit such knowledge remains an open question. Based on the text-to-text transfer transformer (T5) model, this work explores a templa…
We propose the use of a word-level sentiment bidirectional LSTM in tandem with the self-attention mechanism for sentence-level sentiment prediction. In addition to the proposed model, we also present a finance report dataset for sentence-level financial risk detection. Experiments conducted on the p…
In this paper, we propose a novel optimization criterion that leverages features of the skew normal distribution to better model the problem of personalized recommendation. Specifically, the developed criterion borrows the concept and the flexibility of the skew normal distribution, based on which t…
This paper attempts to conduct a sentence-level sentiment analysis with respect to financial risk on a collection of financial reports. Specifically, we first propose a simple yet efficient algorithm to generate financial sentiment phrases (senti-phrases), and then with the obtained senti-phrases, w…