We tackle Extreme Multi-Label Text Classification (XMTC), which involves assigning relevant labels to texts from a huge label space. Attempting to optimize the underexplored tail-head trade-off, we address the XMTC task through its core challenges of volume, skewness, and quality by proposing xCoRetriev, a novel two-stage retrieving and fusing ranking pipeline. Our pipeline addresses the volume challenge by dynamically slicing the large label space; it also tackles the skewness challenge by favoring the tail labels while fusing sparse and dense retrievers. Finally, xCoRetriev faces the quality challenge by enhancing the label space with Retrieval-Augmented Generated (RAG)-labels. Our experiments with four XMTC benchmarks with hundreds of thousands of text documents and labels against six state-of-the-art XMTC baselines demonstrate xCoRetriev's strengths in terms of: (i)~scalability for large label spaces, being among the most efficient methods at training and prediction; (ii)~effectiveness in the face of high skewness, with gains of up to 48% in propensity-scored metrics against the best state-of-the-art baselines; and (iii)~capability of handling very noisy datasets by exploiting RAG-labels.

Digital Collectible Card Games (DCCG) rely on human expertise to create competitive card decks. The most effective decks are shared and become popular among players through online communities. Players then keep enhancing those decks by replacing cards and testing those new deck variations in online arenas. This fine-tuning process is time-consuming and most of the time leads to small improvements in the players win rate. This paper presents CreativeStone, a creativity booster for Hearthstone card decks. Our creativity-guided approach uses a genetic algorithm and the Regent-Dependent Creativity (RDC) metric to identify the core cards of an existing deck, and then improves it towards a more valuable and novel deck by adding cards that are synergic to the core cards, but also different from the ones in the original deck. Our experimental results show that CreativeStone can boost even legendary decks, outperforming handcrafted ones by 21% in win rate.

Transformer-based deep learning is currently the state-of-the-art in many NLP and IR tasks. However, fine-tuning such Transformers for specific tasks, especially in scenarios of ever-expanding volumes of data with constant re-training requirements and budget constraints, is costly (computationally and financially) and energy-consuming. In this paper, we focus on Instance Selection (IS) - a set of methods focused on selecting the most representative documents for training, aimed at maintaining (or improving) classification effectiveness while reducing total time for training (or fine-tuning). We propose E2SC-IS -- Effective, Efficient, and Scalable Confidence-Based IS -- a two-step framework with a particular focus on Transformers and large datasets. E2SC-IS estimates the probability of each instance being removed from the training set based on scalable, fast, and calibrated weak classifiers. E2SC-IS also exploits iterative heuristics to estimate a near-optimal reduction rate. Our solution can reduce the training sets by 29% on average while maintaining the effectiveness in all datasets, with speedup gains up to 70%, scaling for very large datasets (something that the baselines cannot do).

In recent years, online digital games have left behind the status of entertainment sources to become also professional electronic sports. Worldwide championships offer prizes up to millions of dollars for the best competitors and/or teams among different game categories such as digital collectible card games (DCCG), multiplayer online battle arena, etc. Hearthstone, by Blizzard Entertainment, is a DCCG that has an increasing number of players up to the millions. In this game, individual players compete in one-versus-one matches in alternating turns, until a player is defeated. The greatest challenge in this game is to build a deck of cards and a strategy to combine these cards in order to be competitive against other players without a priori knowledge about their decks and strategies. This is a daunting task that requires deep knowledge of each existing card and great amount of creativity to surprise adversaries in this very adaptive environment. This paper presents a computational system, called HoningStone, that automatically generates creative card combos based on the honing theory of creativity. Our experimental results show that HoningStone can generate combos that are more creative than a greedy randomized algorithm driven by a creativity metric.