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In this work, we introduce several schemes to leverage description-augmented embedding similarity for dataless intent classification using current state-of-the-art (SOTA) text embedding models. We report results of our methods on four commonly used intent classification datasets and compare against previous works of a similar nature. Our work shows promising results for dataless classification scaling to a large number of unseen intents. We show competitive results and significant improvements (+6.12\% Avg.) over strong zero-shot baselines, all without training on labelled or task-specific data. Furthermore, we provide qualitative error analysis of the shortfalls of this methodology to help guide future research in this area., Comment: Accepted to the 6th NLP for Conversational AI Workshop at ACL 2024(NLP4ConvAI)

Understanding various humour styles is essential for comprehending the multifaceted nature of humour and its impact on fields such as psychology and artificial intelligence. This understanding has revealed that humour, depending on the style employed, can either have therapeutic or detrimental effects on an individual's health and relationships. Although studies dedicated exclusively to computational-based humour style analysis remain somewhat rare, an expansive body of research thrives within related task, particularly binary humour and sarcasm recognition. In this systematic literature review (SLR), we survey the landscape of computational techniques applied to these related tasks and also uncover their fundamental relevance to humour style analysis. Through this study, we unveil common approaches, illuminate various datasets and evaluation metrics, and effectively navigate the complex terrain of humour research. Our efforts determine potential research gaps and outlined promising directions. Furthermore, the SLR identifies a range of features and computational models that can seamlessly transition from related tasks like binary humour and sarcasm detection to invigorate humour style classification. These features encompass incongruity, sentiment and polarity analysis, ambiguity detection, acoustic nuances, visual cues, contextual insights, and more. The computational models that emerge contain traditional machine learning paradigms, neural network architectures, transformer-based models, and specialised models attuned to the nuances of humour. Finally, the SLR provides access to existing datasets related to humour and sarcasm, facilitating the work of future researchers.

Investors are interested in predicting future success of startup companies, preferably using publicly available data which can be gathered using free online sources. Using public-only data has been shown to work, but there is still much room for improvement. Two of the best performing prediction experiments use 17 and 49 features respectively, mostly numeric and categorical in nature. In this paper, we significantly expand and diversify both the sources and the number of features (to 171) to achieve better prediction. Data collected from Crunchbase, the Google Search API, and Twitter (now X) are used to predict whether a company will raise a round of funding within a fixed time horizon. Much of the new features are textual and the Twitter subset include linguistic metrics such as measures of passive voice and parts-of-speech. A total of ten machine learning models are also evaluated for best performance. The adaptable model can be used to predict funding 1-5 years into the future, with a variable cutoff threshold to favor either precision or recall. Prediction with comparable assumptions generally achieves F scores above 0.730 which outperforms previous attempts in the literature (0.531), and does so with fewer examples. Furthermore, we find that the vast majority of the performance impact comes from the top 18 of 171 features which are mostly generic company observations, including the best performing individual feature which is the free-form text description of the company., Comment: 9 pages. Accepted and presented at ICAIF 2023: ACM International Conference on AI in Finance, Workshop on NLP and Network Analysis, Nov. 27, 2023

In this work, we present a new dataset and a computational strategy for a digital coach that aims to guide users in practicing the protocols of self-attachment therapy. Our framework augments a rule-based conversational agent with a deep-learning classifier for identifying the underlying emotion in a user's text response, as well as a deep-learning assisted retrieval method for producing novel, fluent and empathetic utterances. We also craft a set of human-like personas that users can choose to interact with. Our goal is to achieve a high level of engagement during virtual therapy sessions. We evaluate the effectiveness of our framework in a non-clinical trial with N=16 participants, all of whom have had at least four interactions with the agent over the course of five days. We find that our platform is consistently rated higher for empathy, user engagement and usefulness than the simple rule-based framework. Finally, we provide guidelines to further improve the design and performance of the application, in accordance with the feedback received.

Despite some recent advances, automatic text summarization remains unreliable, elusive, and of limited practical use in applications. Two main problems with current summarization methods are well known: evaluation and factual consistency. To address these issues, we propose a new automatic reference-less text summarization evaluation system that can measure the quality of any text summarization model with a set of generated facts based on factual consistency, comprehensiveness, and compression rate. As far as we know, our evaluation system is the first system that measures the overarching quality of the text summarization models based on factuality, information coverage, and compression rate.

In less than a year's time, March 2022 will mark the twentieth anniversary of the first documented game jam, the Indie Game Jam, which took place in Oakland, California in 2002. Initially, game jams were widely seen as frivolous activities. Since then, they have taken the world by storm. Game jams have not only become part of the day-to-day process of many game developers, but jams are also used for activist purposes, for learning and teaching, as part of the experience economy, for making commercial prototypes that gamers can vote on, and more. Beyond only surveying game jams and the relevant published scientific literature from the last two decades, this paper has several additional contributions. It builds a history of game jams, and proposes two different taxonomies of game jams - a historical and a categorical. In addition, it discusses the definition of game jam and identifies the most active research areas within the game jam community such as the interplay and development with local communities, the study and analysis of game jammers and organisers, and works that bring a critical look on game jams.

In US State government legislatures, most of the activity occurs in committees made up of lawmakers discussing bills. When analyzing, classifying or summarizing these committee proceedings, some important features become broadly interesting. In this paper, we engineer four useful features, two applying to lawmakers (engagement and absence), and two to non-lawmakers (stance and affiliation). We propose a system to automatically track the affiliation of organizations in public comments and whether the organizational representative supports or opposes the bill. The model tracking affiliation achieves an F1 of 0.872 while the support determination has an F1 of 0.979. Additionally, a metric to compute legislator engagement and absenteeism is also proposed and as proof-of-concept, a list of the most and least engaged legislators over one full California legislative session is presented., Comment: 6 pages, 1 figure, 5 equations, 7 tables

Since their inception at the Indie Game Jam in 2002, a significant part of game jams has been knowledge sharing and showcasing ideas and work to peers. While various licensing mechanisms have been used for game jams throughout the years, there has never been a licence uniquely designed for artifacts created during a game jam. In this paper, we present to the community the Game Jam License (GJL) which is designed to facilitate that sharing and knowledge transfer, while making sure the original creators retain commercial rights. The Global Game Jam, since 2009, strives to formalise sharing in a similar manner, by having jammers upload and license their creations under Creative Commons Non Commercial Share Alike 3.0 free license. However, the CC family of licenses is not well suited for software. CC is not compatible with most other licenses, and introduces a legal grey area with the division between commercial and non-commercial use. Moreover, open source licences like GPL are well suited for source code, but not for art and design content. Instead the GJL presented in this paper, aims to uphold the original ideas of game jams (sharing and knowledge transfer), while still allowing the original team to hold on to all rights to their creation, without any of the deficiencies of the CC family of licenses.

We propose a game development framework capable of governing the behavior of complementary companions in a video game. A "complementary" action is contrasted with a mimicking action and is defined as any action by a friendly non-player character that furthers the player's strategy. This is determined through a combination of both player action and game state prediction processes while allowing the AI companion to experiment. We determine the location of interest for companion actions based on a dynamic set of regions customized to the individual player. A user study shows promising results; a majority of participants familiar with game design react positively to the companion behavior, stating that they would consider using the frame-work in future games themselves.