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Found 22 papers, 4 papers with code
BayesJudge: Bayesian Kernel Language Modelling with Confidence Uncertainty in Legal Judgment Prediction
Predicting legal judgments with reliable confidence is paramount for responsible legal AI applications.
Would You Trust an AI Doctor? Building Reliable Medical Predictions with Kernel Dropout Uncertainty
The growing capabilities of AI raise questions about their trustworthiness in healthcare, particularly due to opaque decision-making and limited data availability.
Proceedings of the 3rd International Workshop on Mining and Learning in the Legal Domain (MLLD-23)
This is the Proceedings of the 3rd International Workshop on Mining and Learning in the Legal Domain (MLLD-23) which took place in conjunction with the 32nd ACM International Conference on Information and Knowledge Management (CIKM-2023) at the University of Birmingham, Birmingham, UK on Sunday 22nd October 2023.
PerCoNet: News Recommendation with Explicit Persona and Contrastive Learning
Personalized news recommender systems help users quickly find content of their interests from the sea of information.
Context-Aware Classification of Legal Document Pages
For many business applications that require the processing, indexing, and retrieval of professional documents such as legal briefs (in PDF format etc.
Making a Computational Attorney
This "blue sky idea" paper outlines the opportunities and challenges in data mining and machine learning involving making a computational attorney -- an intelligent software agent capable of helping human lawyers with a wide range of complex high-level legal tasks such as drafting legal briefs for the prosecution or defense in court.
TegFormer: Topic-to-Essay Generation with Good Topic Coverage and High Text Coherence
Creating an essay based on a few given topics is a challenging NLP task.
A Survey on Knowledge-Enhanced Pre-trained Language Models
Natural Language Processing (NLP) has been revolutionized by the use of Pre-trained Language Models (PLMs) such as BERT.
Forgetting Fast in Recommender Systems
In this paper, we investigate fast machine unlearning techniques for recommender systems that can remove the effect of a small amount of training data from the recommendation model without incurring the full cost of retraining.
Micro-Behavior Encoding for Session-based Recommendation
Specifically, we identify two different patterns of micro-behaviors: "sequential patterns" and "dyadic relational patterns".
Grounding Natural Language Instructions: Can Large Language Models Capture Spatial Information?
Even though BERT and similar pre-trained language models have excelled in several NLP tasks, their use has not been widely explored for the UI grounding domain.
Recommendation Fairness: From Static to Dynamic
Driven by the need to capture users' evolving interests and optimize their long-term experiences, more and more recommender systems have started to model recommendation as a Markov decision process and employ reinforcement learning to address the problem.
Process Discovery for Structured Program Synthesis
A core task in process mining is process discovery which aims to learn an accurate process model from event log data.
Proceedings of the AAAI-20 Workshop on Intelligent Process Automation (IPA-20)
This is the Proceedings of the AAAI-20 Workshop on Intelligent Process Automation (IPA-20) which took place in New York, NY, USA on February 7th 2020.
SCRAM: Spatially Coherent Randomized Attention Maps
The central idea of SCRAM is to employ PatchMatch, a randomized correspondence algorithm, to quickly pinpoint the most compatible key (argmax) for each query first, and then exploit that knowledge to design a sparse approximation to non-local mean operations.
Market Trend Prediction using Sentiment Analysis: Lessons Learned and Paths Forward
Financial market forecasting is one of the most attractive practical applications of sentiment analysis.
Factorized Q-Learning for Large-Scale Multi-Agent Systems
Deep Q-learning has achieved a significant success in single-agent decision making tasks.
Multiagent Systems
Bootstrap Domain-Specific Sentiment Classifiers from Unlabeled Corpora
There is often the need to perform sentiment classification in a particular domain where no labeled document is available.
Probabilistic Verb Selection for Data-to-Text Generation
In data-to-text Natural Language Generation (NLG) systems, computers need to find the right words to describe phenomena seen in the data.
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
This paper provides a unified account of two schools of thinking in information retrieval modelling: the generative retrieval focusing on predicting relevant documents given a query, and the discriminative retrieval focusing on predicting relevancy given a query-document pair.
The Anatomy of a Search and Mining System for Digital Archives
Samtla (Search And Mining Tools with Linguistic Analysis) is a digital humanities system designed in collaboration with historians and linguists to assist them with their research work in quantifying the content of any textual corpora through approximate phrase search and document comparison.
Wikipedia Edit Number Prediction based on Temporal Dynamics Only
In this paper, we describe our approach to the Wikipedia Participation Challenge which aims to predict the number of edits a Wikipedia editor will make in the next 5 months.
