3 research outputs found
Interactive Text Generation
Users interact with text, image, code, or other editors on a daily basis.
However, machine learning models are rarely trained in the settings that
reflect the interactivity between users and their editor. This is
understandable as training AI models with real users is not only slow and
costly, but what these models learn may be specific to user interface design
choices. Unfortunately, this means most of the research on text, code, and
image generation has focused on non-interactive settings, whereby the model is
expected to get everything right without accounting for any input from a user
who may be willing to help.
We introduce a new Interactive Text Generation task that allows training
generation models interactively without the costs of involving real users, by
using user simulators that provide edits that guide the model towards a given
target text. We train our interactive models using Imitation Learning, and our
experiments against competitive non-interactive generation models show that
models trained interactively are superior to their non-interactive
counterparts, even when all models are given the same budget of user inputs or
edits.Comment: EMNLP 202
On the impact of publicly available news and information transfer to financial markets
We quantify the propagation and absorption of large-scale publicly available news articles from the World Wide Web to financial markets. To extract publicly available information, we use the news archives from the Common Crawl, a non-profit organization that crawls a large part of the web. We develop a processing pipeline to identify news articles associated with the constituent companies in the S&P 500 index, an equity market index that measures the stock performance of US companies. Using machine learning techniques, we extract sentiment scores from the Common Crawl News data and employ tools from information theory to quantify the information transfer from public news articles to the US stock market. Furthermore, we analyse and quantify the economic significance of the news-based information with a simple sentiment-based portfolio trading strategy. Our findings provide support for that information in publicly available news on the World Wide Web has a statistically and economically significant impact on events in financial markets.ISSN:2054-570
Preferences in AI : An overview
P&al031International audienceThis editorial of the special issue "Representing, Processing, and Learning Preferences : Theoretical and Practical Challenges" surveys past and ongoing research on preferences in AI, including references and pointers to the literature. It covers approaches to representation, reasoning and learning of preferences. Methods in AI are contrasted with those in related areas, such as operations research and databases. Finally, we also give a brief introduction to the contents of the special issue