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An Empirical Study of Factors Affecting Language-Independent Models
Scaling existing applications and solutions to multiple human languages has traditionally proven to be difficult, mainly due to the language-dependent nature of preprocessing and feature engineering techniques employed in traditional approaches.
Characterizing machine learning process: A maturity framework
For example, existing machine learning processes cannot address how to define business use cases for an AI application, how to convert business requirements from offering managers into data requirements for data scientists, and how to continuously improve AI applications in term of accuracy and fairness, and how to customize general purpose machine learning models with industry, domain, and use case specific data to make them more accurate for specific situations etc.
Challenge AI Mind: A Crowd System for Proactive AI Testing
The evaluation shows that the crowd workflow is more effective with the help of machine learning techniques.
Don't get Lost in Negation: An Effective Negation Handled Dialogue Acts Prediction Algorithm for Twitter Customer Service Conversations
Lately, there have been several works proposing a novel taxonomy for fine-grained dialogue acts as well as develop algorithms for automatic detection of these acts.
"How May I Help You?": Modeling Twitter Customer Service Conversations Using Fine-Grained Dialogue Acts
We characterize differences between customer and agent behavior in Twitter customer service conversations, and investigate the effect of testing our system on different customer service industries.
25 Tweets to Know You: A New Model to Predict Personality with Social Media
Predicting personality is essential for social applications supporting human-centered activities, yet prior modeling methods with users written text require too much input data to be realistically used in the context of social media.
