Kais Dukes

Kais Dukes

This paper defines a new approach for augmenting human intelligence with AI for optimal goal solving. Our proposed AI, Indigo, is an acronym for Informed Numerical Decision-making through Iterative Goal-Oriented optimization. When combined with a human collaborator, we term the joint system IndigoVX, for Virtual eXpert. The system is conceptually simple. We envisage this method being applied to games or business strategies, with the human providing strategic context and the AI offering optimal, data-driven moves. Indigo operates through an iterative feedback loop, harnessing the human expert's contextual knowledge and the AI's data-driven insights to craft and refine strategies towards a well-defined goal. Using a quantified three-score schema, this hybridization allows the combined team to evaluate strategies and refine their plan, while adapting to challenges and changes in real-time.

Kais Dukes

Research into statistical parsing for English has enjoyed over a decade of successful results. However, adapting these models to other languages has met with difficulties. Previous comparative work has shown that Modern Arabic is one of the most difficult languages to parse due to rich morphology and free word order. Classical Arabic is the ancient form of Arabic, and is understudied in computational linguistics, relative to its worldwide reach as the language of the Quran. The thesis is based on seven publications that make significant contributions to knowledge relating to annotating and parsing Classical Arabic. A central argument of this thesis is that using a hybrid representation closely aligned to traditional grammar leads to improved parsing for Arabic. To test this hypothesis, two approaches are compared. As a reference, a pure dependency parser is adapted using graph transformations, resulting in an 87.47% F1-score. This is compared to an integrated parsing model with an F1-score of 89.03%, demonstrating that joint dependency-constituency parsing is better suited to Classical Arabic.

Kais Dukes

We describe a contextual parser for the Robot Commands Treebank, a new crowdsourced resource. In contrast to previous semantic parsers that select the most-probable parse, we consider the different problem of parsing using additional situational context to disambiguate between different readings of a sentence. We show that multiple semantic analyses can be searched using dynamic programming via interaction with a spatial planner, to guide the parsing process. We are able to parse sentences in near linear-time by ruling out analyses early on that are incompatible with spatial context. We report a 34% upper bound on accuracy, as our planner correctly processes spatial context for 3,394 out of 10,000 sentences. However, our parser achieves a 96.53% exact-match score for parsing within the subset of sentences recognized by the planner, compared to 82.14% for a non-contextual parser.