An analysis of verbs within video game structures based on a video game verb theory and The Secret of Monkey Island

The aims of this thesis are as followes: to provide a review of the idea presented by three game designers – Chris Crawford, Raph Koster and Anna Anthropy – according to which, verbs should be used to describe the interactions and inner rule structures of video games; present these ideas as a unified theory of verbs within game structures; provide a method for analysing these verbs through syntactic theory presented by Bas Aarts and Van Valin and LaPolla; apply the combined theory of verbs in game structures and the methodology and concepts from syntax to analyse the verbs in the structure of the 1990 point and click adventure game The Secret of Monkey Island and its 2009 Enhanced edition.http://www.ester.ee/record=b4678178*es

Cross-linguistic and cross-scriptal differences in auditory and visual attentional shifts : comparison between native Cantonese and English speakers

Lallier and colleagues (2010b) put forward a new hypothesis proposing the role of temporal interval between salient units in ones native language in shaping the speed of attentional shift. The present study investigated the applicability of this hypothesis to Cantonese speakers and English speakers by comparing their speed of attentional shift in auditory and visual stream segregation tasks. Contrary to Lallier et al.’s hypothesis, results of stepwise regressions revealed no group difference in the segregation thresholds in both modalities after controlling the participants’ mean reaction time and alerting score in the Flanker task, suggesting that the speed of attentional shift is language-independent. Additionally, this study established the normative data of attentional shift in the typical Cantonese-speaking adults. This information can serve as a basis for evaluating the relevance of “sluggish attentional shift” (SAS) to developmental dyslexia in Chinese with a logographic script, which may provide clinical insights to its diagnosis.published_or_final_versionSpeech and Hearing SciencesBachelorBachelor of Science in Speech and Hearing Science

Evaluating Human-Language Model Interaction

Many real-world applications of language models (LMs), such as writing assistance and code autocomplete, involve human-LM interaction. However, most benchmarks are non-interactive in that a model produces output without human involvement. To evaluate human-LM interaction, we develop a new framework, Human-AI Language-based Interaction Evaluation (HALIE), that defines the components of interactive systems and dimensions to consider when designing evaluation metrics. Compared to standard, non-interactive evaluation, HALIE captures (i) the interactive process, not only the final output; (ii) the first-person subjective experience, not just a third-party assessment; and (iii) notions of preference beyond quality (e.g., enjoyment and ownership). We then design five tasks to cover different forms of interaction: social dialogue, question answering, crossword puzzles, summarization, and metaphor generation. With four state-of-the-art LMs (three variants of OpenAI's GPT-3 and AI21 Labs' Jurassic-1), we find that better non-interactive performance does not always translate to better human-LM interaction. In particular, we highlight three cases where the results from non-interactive and interactive metrics diverge and underscore the importance of human-LM interaction for LM evaluation.Comment: Authored by the Center for Research on Foundation Models (CRFM) at the Stanford Institute for Human-Centered Artificial Intelligence (HAI

Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding

Narrative understanding involves capturing the author's cognitive processes, providing insights into their knowledge, intentions, beliefs, and desires. Although large language models (LLMs) excel in generating grammatically coherent text, their ability to comprehend the author's thoughts remains uncertain. This limitation hinders the practical applications of narrative understanding. In this paper, we conduct a comprehensive survey of narrative understanding tasks, thoroughly examining their key features, definitions, taxonomy, associated datasets, training objectives, evaluation metrics, and limitations. Furthermore, we explore the potential of expanding the capabilities of modularized LLMs to address novel narrative understanding tasks. By framing narrative understanding as the retrieval of the author's imaginative cues that outline the narrative structure, our study introduces a fresh perspective on enhancing narrative comprehension