Recently, incorporating structure information (e.g. dependency syntactic tree) can enhance the performance of aspect-based sentiment analysis (ABSA). However, this structure information is obtained from off-the-shelf parsers, which is often sub-optimal and cumbersome. Thus, automatically learning adaptive structures is conducive to solving this problem. In this work, we concentrate on structure induction from pre-trained language models (PLMs) and throw the structure induction into a spectrum perspective to explore the impact of scale information in language representation on structure induction ability. Concretely, the main architecture of our model is composed of commonly used PLMs (e.g. RoBERTa, etc), and a simple yet effective graph structure learning (GSL) module (graph learner + GNNs). Subsequently, we plug in spectral filters with different bands respectively after the PLMs to produce filtered language representations and feed them into the GSL module to induce latent structures. We conduct extensive experiments on three public benchmarks for ABSA. The results and further analyses demonstrate that introducing this spectral approach can shorten Aspects-sentiment Distance (AsD) and be beneficial to structure induction. Even based on such a simple framework, the effects on three datasets can reach SOTA (state of the art) or near SOTA performance. Additionally, our exploration also has the potential to be generalized to other tasks or to bring inspiration to other similar domains.

Financial volatility prediction is vital for indicating a company’s risk profile. Transcripts of companies’ earnings calls are important unstructured data sources to be utilized to access companies’ performance and risk profiles. However, current works ignore the role of financial metrics knowledge (such as EBIT, EPS, and ROI) in transcripts, which is crucial for understanding companies’ performance, and little consideration is given to integrating text and price information. In this work, we statistic common financial metrics and make a special dataset based on these metrics. Then, we introduce a knowledge-enhanced financial volatility prediction method (KeFVP) to inject knowledge of financial metrics into text comprehension by knowledge-enhanced adaptive pre-training (KePt) and effectively incorporating text and price information by introducing a conditional time series prediction module. We conduct extensive experiments on three real-world public datasets, and the results indicate that KeFVP is effective and outperforms all the state-of-the-art methods.