This paper presents an interpretable legal judgment reasoning framework that aims to improve both the accuracy and interpretability of legal judgment predictions. The framework covers five key areas: limitations of existing methods, the role of factual logic in judgments, integration of external legal knowledge, handling of long-tail and ambiguous cases, and overall interpretability. The methodology adopted is qualitative, involving experimental data analysis and user feedback. This framework combines factually based logic with legal knowledge using a chain prompt reasoning module and a contrastive knowledge fusing module. Therefore, the result shows considerable improvement in terms of prediction accuracy and interpretability. These advances will fill important gaps in the existing literature on LJP research and represent a dynamic, transparent approach to judicial decision-making.
Earnings conference calls are among the most significant sources of information regarding a firm's financial performance and strategic outlook. However, growing transcripts length make it difficult to manually analyze. This paper investigates the potential of ECT-SKIE, a self-supervised extractive summarization model, in addressing these challenges. This work systematically assesses the performance of ECT-SKIE in extracting key insights, the efficiency of ECT-SKIE compared with traditional methods, and the application of advanced techniques such as variational information bottleneck theory and structure-aware contrastive learning to improve the model's performance. Besides, the effectiveness of the container-based key sentence extractor in redundancy reduction is emphasized. A large-scale dataset of U.S. market earnings call transcripts is leveraged to verify the model with the ability of ECT-SKIE in significantly improving the accuracy, efficiency, and clarity of the extraction, making it a standard for automated financial analysis.
This problem has unique challenges for few-shot multi-hop reasoning in TKGs, considering that the graph is dynamic and previous methods mainly focused on static graphs. In this paper, a reinforcement learning framework is integrated with advanced path search strategies to enhance the accuracy of reasoning, entity representation of tasks, and interpretability in TKGs. Five research hypotheses are considered: the effect of reinforcement learning, the contribution of one-hop neighbors, the efficacy of path search strategies, the relationship between the existing paths and the current state, and the contribution of path analysis to better interpretability. Quantitative methodologies are used with benchmark datasets, such as ICEWS18-few, ICEWS14-few, and GDELT-few. The results indicate that the framework improves the reasoning process and reduces computational complexity. These findings address the current gaps in TKG reasoning research and lay a foundation for advancing dynamic reasoning approaches in knowledge graphs.
The paper provides insights into the obstacles in automatic table extraction from PDF-based journal articles with a focus on optimizing detection accuracy and minimizing the loss of information. The impact of the text size, border length, absolute location, and hierarchical clustering on compared performance with the previously developed solutions is studied. This paper adopted a quantitative research approach to explore how changes in independent variables influence detection accuracy and extraction efficiency. The results show that optimized text size and flexible border length greatly improve the detection and restoration of table structures, while hierarchical clustering improves the accuracy of table structures. The proposed method outperforms previous techniques in terms of reducing information loss and improving efficiency, and it is promising for automated data extraction in academic documents.
This study investigates the improvements in speech compression efficiency, particularly at low bitrates, by addressing the challenges of redundancy removal and enhancing context awareness. The research proposes IBACodec, a novel codec that leverages advanced attention mechanisms, such as the intra-inter broad transformer, and a dual-branch conformer for efficient redundancy elimination. Five core hypotheses were tested: the impact of context awareness, the effectiveness of dual-branch modeling, a comparative analysis with existing codecs, subjective evaluations, and objective metric performance. Results demonstrate that IBACodec outperforms traditional codecs like SoundStream and Opus in compression efficiency and quality at lower bitrates. Furthermore, subjective assessments reveal superior performance at low bitrates, while objective metrics such as ViSQOL, LLR, and CEP also confirm the codec's advantages. This research highlights the potential of IBACodec as a leading solution in speech compression, emphasizing the role of advanced machine learning techniques in enhancing codec performance.