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Found 15 papers, 3 papers with code
ProcessGPT: Transforming Business Process Management with Generative Artificial Intelligence
We introduce ProcessGPT as a new technology that has the potential to enhance decision-making in data-centric and knowledge-intensive processes.
Debiasing Pretrained Text Encoders by Paying Attention to Paying Attention
Recent studies in fair Representation Learning have observed a strong inclination for natural language processing (NLP) models to exhibit discriminatory stereotypes across gender, religion, race and many such social constructs.
Crowdsourcing Diverse Paraphrases for Training Task-oriented Bots
A prominent approach to build datasets for training task-oriented bots is crowd-based paraphrasing.
A Query Language for Summarizing and Analyzing Business Process Data
In the context of business processes, we consider the Big Data problem as a massive number of interconnected data islands from personal, shared and business data.
Automatic Generation of Chatbots for Conversational Web Browsing
In this paper, we describe the foundations for generating a chatbot out of a website equipped with simple, bot-specific HTML annotations.
A Study of Incorrect Paraphrases in Crowdsourced User Utterances
Developing bots demands highquality training samples, typically in the form of user utterances and their associated intents.
DARec: Deep Domain Adaptation for Cross-Domain Recommendation via Transferring Rating Patterns
Cross-domain recommendation has long been one of the major topics in recommender systems.
Combining Crowd and Machines for Multi-predicate Item Screening
This paper discusses how crowd and machine classifiers can be efficiently combined to screen items that satisfy a set of predicates.
Adversarial Collaborative Auto-encoder for Top-N Recommendation
In this work, to address the above issue, we propose a general adversial training framework for neural network-based recommendation models, which improves both the model robustness and the overall performance.
GrCAN: Gradient Boost Convolutional Autoencoder with Neural Decision Forest
We develop a gradient boost module and embed it into the proposed convolutional autoencoder with neural decision forest to improve the performance.
A Unified Knowledge Representation and Context-aware Recommender System in Internet of Things
Within the rapidly developing Internet of Things (IoT), numerous and diverse physical devices, Edge devices, Cloud infrastructure, and their quality of service requirements (QoS), need to be represented within a unified specification in order to enable rapid IoT application development, monitoring, and dynamic reconfiguration.
Opinion Fraud Detection via Neural Autoencoder Decision Forest
Online reviews play an important role in influencing buyers' daily purchase decisions.
Crowd-Machine Collaboration for Item Screening
In this paper we describe how crowd and machine classifier can be efficiently combined to screen items that satisfy a set of predicates.
Data Curation APIs
For deeper interpretation of and better intelligence with big data, it is important to transform raw data (unstructured, semi-structured and structured data sources, e. g., text, video, image data sets) into curated data: contextualized data and knowledge that is maintained and made available for use by end-users and applications.
Big Data and Cross-Document Coreference Resolution: Current State and Future Opportunities
Information Extraction (IE) is the task of automatically extracting structured information from unstructured/semi-structured machine-readable documents.
coreference-resolution
Cross Document Coreference Resolution
