Multi-level sentence simplification generates simplified sentences with varying language proficiency levels.We propose Label Confidence Weighted Learning (LCWL), a novel approach that incorporates a label confidence weighting scheme in the training loss of the encoderdecoder model, setting it apart from existing confidence-weighting methods primarily designed for classification.Experimentation on English grade-level simplification dataset shows that LCWL outperforms state-of-the-art unsupervised baselines.Fine-tuning the LCWL model on in-domain data and combining with Symmetric Cross Entropy (SCE) consistently delivers better simplifications compared to strong supervised methods.Our results highlight the effectiveness of label confidence weighting techniques for text simplification tasks with encoderdecoder architectures.

Xinying Qiu, Yuan Chen, Hanwu Chen, Jian-Yun Nie, Yuming Shen, Dawei Lu. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Text mining and machine learning methodologies have been applied toward knowledge discovery in several domains, such as biomedicine and business. Interestingly, in the business domain, the text mining and machine learning community has minimally explored company annual reports with their mandatory disclosures. In this study, we explore the question “How can annual reports be used to predict change in company performance from one year to the next?” from a text mining perspective. Our article contributes a systematic study of the potential of company mandatory disclosures using a computational viewpoint in the following aspects: (a) We characterize our research problem along distinct dimensions to gain a reasonably comprehensive understanding of the capacity of supervised learning methods in predicting change in company performance using annual reports, and (b) our findings from unbiased systematic experiments provide further evidence about the economic incentives faced by analysts in their stock recommendations and speculations on analysts having access to more information in producing earnings forecast.

BACKGROUND: Annotating genes and their products with Gene Ontology codes is an important area of research. One approach is to use the information available about these genes in the biomedical literature. The goal in this paper, based on this approach, is to develop automatic annotation methods that can supplement the expensive manual annotation processes currently in place. RESULTS: Using a set of Support Vector Machines (SVM) classifiers we were able to achieve Fscores of 0.49, 0.41 and 0.33 for codes of the molecular function, cellular component and biological process GO hierarchies respectively. We find that alternative term weighting strategies are not different from each other in performance and feature selection strategies reduce performance. The best thresholding strategy is one where a single threshold is picked for each hierarchy. Hierarchy level is important especially for molecular function and biological process. The cellular component hierarchy stands apart from the other two in many respects. This may be due to fundamental differences in link semantics. This research shows that it is possible to beneficially exploit the hierarchical structures by defining and testing a relaxed criteria for classification correctness. Finally it is possible to build classifiers for codes with very few associated documents but as expected a huge penalty is paid in performance. CONCLUSION: The GO annotation problem is complex. Several key observations have been made as for example about topic drift that may be important to consider in annotation strategies.