A Novel Perspective to Look At Attention: Bi-level Attention-based Explainable Topic Modeling for News Classification

Many recent deep learning-based solutions have widely adopted the attention-based mechanism in various tasks of the NLP discipline. However, the inherent characteristics of deep learning models and the flexibility of the attention mechanism increase the models' complexity, thus leading to challenges in model explainability. In this paper, to address this challenge, we propose a novel practical framework by utilizing a two-tier attention architecture to decouple the complexity of explanation and the decision-making process. We apply it in the context of a news article classification task. The experiments on two large-scaled news corpora demonstrate that the proposed model can achieve competitive performance with many state-of-the-art alternatives and illustrate its appropriateness from an explainability perspective.Comment: Findings of ACL202

Enhancing Topic Extraction in Recommender Systems with Entropy Regularization

In recent years, many recommender systems have utilized textual data for topic extraction to enhance interpretability. However, our findings reveal a noticeable deficiency in the coherence of keywords within topics, resulting in low explainability of the model. This paper introduces a novel approach called entropy regularization to address the issue, leading to more interpretable topics extracted from recommender systems, while ensuring that the performance of the primary task stays competitively strong. The effectiveness of the strategy is validated through experiments on a variation of the probabilistic matrix factorization model that utilizes textual data to extract item embeddings. The experiment results show a significant improvement in topic coherence, which is quantified by cosine similarity on word embeddings

Topic-Centric Explanations for News Recommendation

News recommender systems (NRS) have been widely applied for online news websites to help users find relevant articles based on their interests. Recent methods have demonstrated considerable success in terms of recommendation performance. However, the lack of explanation for these recommendations can lead to mistrust among users and lack of acceptance of recommendations. To address this issue, we propose a new explainable news model to construct a topic-aware explainable recommendation approach that can both accurately identify relevant articles and explain why they have been recommended, using information from associated topics. Additionally, our model incorporates two coherence metrics applied to assess topic quality, providing measure of the interpretability of these explanations. The results of our experiments on the MIND dataset indicate that the proposed explainable NRS outperforms several other baseline systems, while it is also capable of producing interpretable topics compared to those generated by a classical LDA topic model. Furthermore, we present a case study through a real-world example showcasing the usefulness of our NRS for generating explanations.Comment: 20 pages, submitted to a journa

Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations

Precisely recommending candidate news articles to users has always been a core challenge for personalized news recommendation systems. Most recent works primarily focus on using advanced natural language processing techniques to extract semantic information from rich textual data, employing content-based methods derived from local historical news. However, this approach lacks a global perspective, failing to account for users' hidden motivations and behaviors beyond semantic information. To address this challenge, we propose a novel model called GLORY (Global-LOcal news Recommendation sYstem), which combines global representations learned from other users with local representations to enhance personalized recommendation systems. We accomplish this by constructing a Global-aware Historical News Encoder, which includes a global news graph and employs gated graph neural networks to enrich news representations, thereby fusing historical news representations by a historical news aggregator. Similarly, we extend this approach to a Global Candidate News Encoder, utilizing a global entity graph and a candidate news aggregator to enhance candidate news representation. Evaluation results on two public news datasets demonstrate that our method outperforms existing approaches. Furthermore, our model offers more diverse recommendations.Comment: 10 pages, Recsys 202

The life cycle of initial public offering companies in China

Purpose – The purpose of this paper is to identify the extent to which the company’s post- initial public offering (IPO) outcome varies, along with the determinants of the post-IPO outcomes. Design/methodology/approach – The authors use Cox proportional hazards models to examine what determines the company’s post-IPO transition to one of the classified outcomes, delisting, acquisition due to strong performance, and acquisition due to weak performance. The authors develop models taking in a range of information concerning pre-IPO characteristics, offering characteristics, financial indicators, company specifics, industry features, and corporate ownership and governance. Findings – Delisting is predominantly influenced by the company’ pre-IPO operating performance, as well as financial indicators and governance structure at the time of the IPO. Sound governance structure and good financial standing of the company aid it to achieve its goal. Mergers and acquisitions (M&As) of both forms are distinguished most significantly by ownership structure and industry features, which is consonant with the position that M&As are majorly motivated by social concerns and corporate control considerations. Centrally, corporate evolution is jointly shaped by market force and state control. Practical implications – The findings can inform public policy decisions. There is a case for gradual introduction of institutional changes which facilitate, regulate, and monitor orderly market operations in line with the market mechanism and sound corporate governance. Originality/value – The study is among the first efforts to examine what determines the company’s transition to one of the post-IPO states following the IPO in China’s stock market. Keywords China, Mergers and acquisitions, Listing, IPO, Corporate ownership and governance, Cox hazard function, Delisting, Agency costs Paper type Research pape

Large Language Models on Wikipedia-Style Survey Generation: an Evaluation in NLP Concepts

Gao F, Jiang H, Blum M, et al. Large Language Models on Wikipedia-Style Survey Generation: an Evaluation in NLP Concepts. arXiv:2308.10410. 2023.Large Language Models (LLMs) have achieved significant success across various natural language processing (NLP) tasks, encompassing question-answering, summarization, and machine translation, among others. While LLMs excel in general tasks, their efficacy in domain-specific applications remains under exploration. Additionally, LLM-generated text sometimes exhibits issues like hallucination and disinformation. In this study, we assess LLMs' capability of producing concise survey articles within the computer science-NLP domain, focusing on 20 chosen topics. Automated evaluations indicate that GPT-4 outperforms GPT-3.5 when benchmarked against the ground truth. Furthermore, four human evaluators provide insights from six perspectives across four model configurations. Through case studies, we demonstrate that while GPT often yields commendable results, there are instances of shortcomings, such as incomplete information and the exhibition of lapses in factual accuracy