Identifying proxy issuers and bonds is a critical but largely manual step in illiquid corporate bond pricing, requiring analysts to navigate sparse public information and inconsistent industry signals. We present a modular pipeline for proxy retrieval that integrates issuer profiling with hierarchical industry classification and multi-stage retrieval over issuers. We evaluate alternative design choices across hierarchical classification and reranking stages to understand how structured representations and industry-aware signals influence performance. Experiments on a large internal dataset show that domain-adapted embeddings and explicit industry hierarchy signals are primary drivers of quality across stages. The evaluated system produces issuer-level proxy shortlists rather than final bond prices. Pilot deployment experience highlights the value of modular, cost-aware designs for robust proxy retrieval in enterprise settings.

Mitigating entity bias is a critical challenge in Relation Extraction (RE), where models often rely excessively on entities, resulting in poor generalization. This paper presents a novel approach to address this issue by adapting a Variational Information Bottleneck (VIB) framework. Our method compresses entity-specific information while preserving task-relevant features. It achieves state-of-the-art performance on both general and financial domain RE datasets, excelling in in-domain settings (original test sets) and out-of-domain (modified test sets with type-constrained entity replacements). Our approach offers a robust, interpretable, and theoretically grounded methodology.

Existing studies on using social media for deriving mental health status of users focus on the depression detection task. However, for case management and referral to psychiatrists, health-care workers require practical and scalable depressive disorder screening and triage system. This study aims to design and evaluate a decision support system (DSS) to reliably determine the depressive triage level by capturing fine-grained depressive symptoms expressed in user tweets through the emulation of the Patient Health Questionnaire-9 (PHQ-9) that is routinely used in clinical practice. The reliable detection of depressive symptoms from tweets is challenging because the 280-character limit on tweets incentivizes the use of creative artifacts in the utterances and figurative usage contributes to effective expression. We propose a novel BERT based robust multi-task learning framework to accurately identify the depressive symptoms using the auxiliary task of figurative usage detection. Specifically, our proposed novel task sharing mechanism,co-task aware attention, enables automatic selection of optimal information across the BERT lay-ers and tasks by soft-sharing of parameters. Our results show that modeling figurative usage can demonstrably improve the model’s robustness and reliability for distinguishing the depression symptoms.