Access to timely and affordable dispute resolution is a major challenge for industries such as insurance, finance, and consumer goods, where legal disputes between suppliers and consumers are frequent and often complex. We present “ArgAssist”, an LLM-based system for structured argumentative discourse generation that forms a core component of an alternative dispute resolution (ADR) platform that we are building. ArgAssist assists parties involved in a dispute (such as insurer and insured in an insurance dispute) by synthesizing discourse-structured legal arguments, represented as claims supported by typed premises grounded in case information. ArgAssist first generates “base” arguments using an LLM, which are then strengthened by grounding them in relevant prior cases and statutes to ensure legal soundness and contextual coherence. We introduce a novel evaluation metric for assessing the quality of generated arguments and demonstrate ArgAssist’s effectiveness on two real-world datasets in the insurance domain. Our results show that explicitly modeling argumentative discourse structure and grounding significantly improves alignment with expert-authored legal arguments.

Disputes between two parties occur in almost all domains such as taxation, insurance, banking, healthcare, etc. The disputes are generally resolved in a specific forum (e.g., consumer court) where facts are presented, points of disagreement are discussed, arguments as well as specific demands of the parties are heard, and finally a human judge resolves the dispute by often favouring one of the two parties. In this paper, we explore the use of large language models (LLMs) as assistants for the human judge to resolve such disputes, as part of our DRAssist system. We focus on disputes from two specific domains – automobile insurance and domain name disputes. DRAssist identifies certain key structural elements (e.g., facts, aspects or disagreement, arguments) of the disputes and summarizes the unstructured dispute descriptions to produce a structured summary for each dispute. We then explore multiple prompting strategies with multiple LLMs for their ability to assist in resolving the disputes in these domains. In DRAssist, these LLMs are prompted to produce the resolution output at three different levels – (i) identifying an overall stronger party in a dispute, (ii) decide whether each specific demand of each contesting party can be accepted or not, (iii) evaluate whether each argument by each contesting party is strong or weak. We evaluate the performance of LLMs on all these tasks by comparing them with relevant baselines using suitable evaluation metrics.

BACKGROUND: Social media is a useful platform to share health-related information due to its vast reach. This makes it a good candidate for public-health monitoring tasks, specifically for pharmacovigilance. We study the problem of extraction of Adverse-Drug-Reaction (ADR) mentions from social media, particularly from Twitter. Medical information extraction from social media is challenging, mainly due to short and highly informal nature of text, as compared to more technical and formal medical reports. METHODS: Current methods in ADR mention extraction rely on supervised learning methods, which suffer from labeled data scarcity problem. The state-of-the-art method uses deep neural networks, specifically a class of Recurrent Neural Network (RNN) which is Long-Short-Term-Memory network (LSTM). Deep neural networks, due to their large number of free parameters rely heavily on large annotated corpora for learning the end task. But in the real-world, it is hard to get large labeled data, mainly due to the heavy cost associated with the manual annotation. RESULTS: To this end, we propose a novel semi-supervised learning based RNN model, which can leverage unlabeled data also present in abundance on social media. Through experiments we demonstrate the effectiveness of our method, achieving state-of-the-art performance in ADR mention extraction. CONCLUSION: In this study, we tackle the problem of labeled data scarcity for Adverse Drug Reaction mention extraction from social media and propose a novel semi-supervised learning based method which can leverage large unlabeled corpus available in abundance on the web. Through empirical study, we demonstrate that our proposed method outperforms fully supervised learning based baseline which relies on large manually annotated corpus for a good performance.

Performance appraisal (PA) is a crucial HR process that enables an organization to periodically measure and evaluate every employee's performance and also to drive performance improvements. In this paper, we describe a novel system called HiSPEED to analyze PA data using automated statistical, datamining and text-mining techniques, to generate novel and actionable insights / patterns and to help in improving the quality and effectiveness of the PA process. The goal is to produce insights that can be used to answer (in part) the crucial "business questions" that HR executives and business leadership face in talent management. The business questions pertain to (i) improving the quality of the goal setting process, (ii) improving the quality of the self-appraisal comments and supervisor feedback comments, (iii) discovering high-quality supervisor suggestions for performance improvements (iv) discovering evidence provided by employees to support their self-assessments (v) measuring the quality of supervisor assessments (vi) understanding the root-causes of poor and exceptional performances (vii) detecting instances of personal and systemic biases and so forth. The paper discusses specially designed algorithms to answer these business questions and illustrates them by reporting the insights produced on a real-life PA dataset from a large multi-national IT services organization.

The notion of roles is crucial in project management across various domains. A role indicates a broad set of tasks, activities, deliverables and responsibilities that the person needs to carry out within a project. Assigning roles to team members clarifies the expectations of work items to be delivered by each and structures the interactions of the team among themselves as well as with external stakeholders. This paper analyzes a sizeable real-life dataset regarding the actual usage of roles in software development and maintenance projects in a large multinational IT organization. The paper introduces and formalizes concepts such as seniority level of a role, career progression and career lines, formulates various business questions related to role-based project management, proposes analytics techniques to answer them and outlines the actual results produced to answer the business questions. The business questions are related to dependencies between roles, patterns in role assignments and durations, predicting role changes, discovering insights useful for meeting career aspirations, interesting role sequences etc. The proposed analytics algorithms are based on Markov models, sequence mining, classification and survival analysis.

Temporal annotation of plain text is considered a useful component of modern information retrieval tasks. In this work, different approaches for identification and classification of temporal expressions in Hindi are developed and analyzed. First, a rule-based approach is developed, which takes plain text as input and based on a set of hand-crafted rules, produces a tagged output with identified temporal expressions. This approach performs with a strict F1-measure of 0.83. In another approach, a CRF-based classifier is trained with human tagged data and is then tested on a test dataset. The trained classifier identifies the time expressions from plain text and further classifies them to various classes. This approach performs with a strict F1-measure of 0.78. Next, the CRF is replaced by an SVM-based classifier and the same experiment is performed with the same features. This approach is shown to be comparable to the CRF and performs with a strict F1-measure of 0.77. Using the rule base information as an additional feature enhances the performances to 0.86 and 0.84 for the CRF and SVM respectively. With three different comparable systems performing the extraction task, merging them to take advantage of their positives is the next step. As the first merge experiment, rule-based tagged data is fed to the CRF and SVM classifiers as additional training data. Evaluation results report an increase in F1-measure of the CRF from 0.78 to 0.8. Second, a voting-based approach is implemented, which chooses the best class for each token from the outputs of the three approaches. This approach results in the best performance for this task with a strict F1-measure of 0.88. In this process a reusable gold standard dataset for temporal tagging in Hindi is also developed. Named the ILTIMEX2012 corpus, it consists of 300 manually tagged Hindi news documents.