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Finite state transducers (FSTs) are a valuable tool in data processing systems, where they are used to realise string-to-string transductions. We consider the problem of inferring transductions representable by FSTs through reinforcement learning. In this machine-learning paradigm, a learning algorithm repeatedly interacts with an environment by performing one out of a fixed set of candidate actions. Each action taken yields a reward, the size of which depends on the environment's current state, and causes the environment to change into a new state. The algorithm's objective is to maximise the accumulated reward. In the setting explored here, the environment consists of the next symbol in the input string to be rewritten and a transducer state. An action consists in choosing the symbol to output next, and the transducer state to shift into, thus causing a change in the environment. We propose a learning algorithm that starts out from a singleton set of states, and every time the learning rate stagnates, splits a state into two. For the split, it chooses a state that has been visited often, but despite this provides little information about how to maximise the reward. We evaluate the algorithm through empirical experiments, and the results suggest that it is robust enough to handle situations where the target transduction changes during the learning process.

Keyword extraction is a foundational task in natural language processing, underpinning countless real-world applications. One of these is contextual advertising, where keywords help predict the topical congruence between ads and their surrounding media contexts to enhance advertising effectiveness. Recent advances in artificial intelligence have improved keyword extraction capabilities but also introduced concerns about computational cost. Moreover, although the end-user experience is of vital importance, human evaluation of keyword extraction performances remains under-explored. This study provides a comparative evaluation of prevalent keyword extraction algorithms with different levels of complexity represented by TF-IDF, KeyBERT, and Llama 2. To evaluate their effectiveness, a mixed-methods approach is employed, combining quantitative benchmarking with qualitative assessments from 855 participants through four survey-based experiments. The findings demonstrate that KeyBERT achieves an effective balance between user preferences and computational efficiency, compared to the other algorithms. We observe a clear overall preference for gold-standard keywords, but there is a misalignment between algorithmic benchmark performance and user ratings. This reveals a long-overlooked gap between traditional precision-focused metrics and user-perceived algorithm efficiency. The study underscores the importance of human-in-the-loop evaluation methodologies and proposes analytical tools to facilitate their implementation.

Artificial intelligence (AI) is revolutionizing the field of programmatic digital marketing. This article provides a forward-looking perspective on AI in advertising and offers a conceptual framework for developing effective marketing strategies. We explore key developments in the industry: the progression from personalized to contextual targeting, and the increased reliance on AI-based automation. Additionally, the article identifies three critical factors – results, resources, and rectitude – that influence the choice of strategies for online advertisers. Our findings suggest that while AI might multiply the effectiveness of programmatic advertising campaigns, it comes with important tradeoffs that must be taken into account. By synthesizing literature from digital advertising, computer science, and media studies, we offer an improved understanding of the evolving programmatic advertising ecosystem and distill this into practical advice for advertisers.