In this study, we use recent stance detection methods to study the stance (for, against or neutral) of statements in official information booklets for voters. Our main goal is to answer the fundamental question: are topics to be voted on presented in a neutral way? To this end, we first train and compare several models for stance detection on a large dataset about Swiss politics. We find that fine-tuning an M-BERT model leads to the best accuracy. We then use our best model to analyze the stance of utterances extracted from the Swiss federal voting booklet concerning the Swiss popular votes of September 2022, which is the main goal of this project. We evaluated the models in both a multilingual as well as a monolingual context for German, French, and Italian. Our analysis shows that some issues are heavily favored while others are more balanced, and that the results are largely consistent across languages. Our findings have implications for the editorial process of future voting booklets and the design of better automated systems for analyzing political discourse.

Enterprises and investors are increasing their use of natural language APIs to assist processing in tasks like data mining for sales intelligence, tracking how marketing campaigns change over time, and better defending against phishing and ransomware attacks. Still, AI products that use natural language engines to analyze text have a long way to go to capture more than a fraction of the nuance humans use to communicate with each other. The company this week announced new advanced features for its cloud-based natural language API designed to help AI developers "[extract] emotions in large-scale texts and [identify] stylometric data driving a complete fingerprint of content," Expert.ai said in a statement. Based in Modena, Italy and with U.S. headquarters in Rockville, Maryland, Expert.ai The company's customers include media outlets like the Associated Press, which uses NL software for content classification and enrichment; business intelligence consultants like L'Argus de la Presse, which conducts brand reputation analysis with NL processing; and financial services firms like Zurich Insurance, which uses Expert.ai's

By learning how to pitch his bot idea while driving a robot, Button.ai Organized by VentureBeat, the international botathon took place July 9-10 in New York, Melbourne, Tel Aviv, and San Francisco. A fifth finalist category was made for people participating online elsewhere in the world. Finals for popular vote and judges' categories were held Tuesday in San Francisco at MobileBeat, a two-day gathering of chatbot and AI leaders, held July 12-13 at The Village. Skoolbot won the portion of the competition decided by judges Phil Libin, an investor in bots from General Catalyst; SmarterChild creator Robert Hoffer; and Alfred Lin, an investor at Sequoia Capital.