Weijun Li

Jingyi Liu, Yanjie Li, Lina Yu et al.

Noise ubiquitously exists in signals due to numerous factors including physical, electronic, and environmental effects. Traditional methods of symbolic regression, such as genetic programming or deep learning models, aim to find the most fitting expressions for these signals. However, these methods often overlook the noise present in real-world data, leading to reduced fitting accuracy. To tackle this issue, we propose \textit{\textbf{D}eep Symbolic Regression against \textbf{N}oise via \textbf{C}ontrastive \textbf{L}earning (DN-CL)}. DN-CL employs two parameter-sharing encoders to embed data points from various data transformations into feature shields against noise. This model treats noisy data and clean data as different views of the ground-truth mathematical expressions. Distances between these features are minimized, utilizing contrastive learning to distinguish between 'positive' noise-corrected pairs and 'negative' contrasting pairs. Our experiments indicate that DN-CL demonstrates superior performance in handling both noisy and clean data, presenting a promising method of symbolic regression.

Yanjie Li, Lina Yu, Weijun Li et al.

Formulas are the language of communication between humans and nature. The discovery of formulas to describe natural laws from observational data is the purpose of scientific research. It is also an important research topic in artificial intelligence, which is called a symbolic regression problem. Most of the existing symbolic regression methods generate expressions directly from observed data. Although in some methods, we can inject some prior knowledge into the model by adding constraints or introducing some special character hints. However, these methods can only introduce a limited amount of prior knowledge specified in advance. Not to mention understanding natural language instructions. In this article, based on the powerful knowledge reserve and language understanding ability of multi-modal large language models, we present ChatSR, which acts like a knowledgeable human scientist, and we can tell it any prior knowledge through natural language to guide it in formula generation. By testing on 13 datasets, ChatSR not only shows state-of-the-art performance on traditional symbolic regression tasks. More notably, ChatSR can well understand the prior knowledge contained in natural language prompts and improve the quality of generated expressions. In addition, it is exciting that ChatSR has a good zero-shot capability to understand prior knowledge that is not present in the training data.

Yaohua Bu, Tianyi Ma, Weijun Li et al.

Second language (L2) English learners often find it difficult to improve their pronunciations due to the lack of expressive and personalized corrective feedback. In this paper, we present Pronunciation Teacher (PTeacher), a Computer-Aided Pronunciation Training (CAPT) system that provides personalized exaggerated audio-visual corrective feedback for mispronunciations. Though the effectiveness of exaggerated feedback has been demonstrated, it is still unclear how to define the appropriate degrees of exaggeration when interacting with individual learners. To fill in this gap, we interview 100 L2 English learners and 22 professional native teachers to understand their needs and experiences. Three critical metrics are proposed for both learners and teachers to identify the best exaggeration levels in both audio and visual modalities. Additionally, we incorporate the personalized dynamic feedback mechanism given the English proficiency of learners. Based on the obtained insights, a comprehensive interactive pronunciation training course is designed to help L2 learners rectify mispronunciations in a more perceptible, understandable, and discriminative manner. Extensive user studies demonstrate that our system significantly promotes the learners' learning efficiency.

Yaohua Bu, Weijun Li, Tianyi Ma et al.

To provide more discriminative feedback for the second language (L2) learners to better identify their mispronunciation, we propose a method for exaggerated visual-speech feedback in computer-assisted pronunciation training (CAPT). The speech exaggeration is realized by an emphatic speech generation neural network based on Tacotron, while the visual exaggeration is accomplished by ADC Viseme Blending, namely increasing Amplitude of movement, extending the phone's Duration and enhancing the color Contrast. User studies show that exaggerated feedback outperforms non-exaggerated version on helping learners with pronunciation identification and pronunciation improvement.