In our rapidly evolving technological landscape, AI tools have gained substantial power and integration across various domains. Through interviews and surveys conducted at a University in the Netherlands, we investigated students’ perceptions of AI tools. Our results show that students generally have a positive attitude towards the adoption of AI technologies and feel that it enhances their learning experience. Furthermore, this research project examines the capabilities of AI-powered tools, namely GitHub Copilot and ChatGPT, in solving a variety of university-level assignments. By empirically evaluating the capabilities of these AI tools and offering insights to educators, this research project aims to assist them in designing programming exercises that encompass essential learning processes while accounting for students’ utilization of AI tools. The findings indicate that a majority of the exercises currently utilized by the examined university could be solved partially or entirely with the aid of these tools. This project highlights the importance of educators understanding the capabilities of AI tools, as well as students’ attitudes towards them, to effectively adapt their teaching methods and promote essential learning goals.

Enrico Liscio, Oscar Araque, Lorenzo Gatti, Ionut Constantinescu, Catholijn Jonker, Kyriaki Kalimeri, Pradeep Kumar Murukannaiah. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Several lexica for sentiment analysis have been developed; while most of these come with word polarity annotations (e.g., positive/negative), attempts at building lexica for finer-grained emotion analysis (e.g., happiness, sadness) have recently attracted significant attention. They are often exploited as a building block for developing emotion recognition learning models, and/or used as baselines to which the performance of the models can be compared. In this work, we contribute two new resources, that we call DepecheMood++ (DM++): a) an extension of an existing and widely used emotion lexicon for English; and b) a novel version of the lexicon, targeting Italian. Furthermore, we show how simple techniques can be used, both in supervised and unsupervised experimental settings, to boost performance on datasets and tasks of varying degree of domain-specificity. Also, we report an extensive comparative analysis against other available emotion lexica and state-of-the-art supervised approaches, showing that DepecheMood++ emerges as the best-performing non-domain-specific lexicon in unsupervised settings. We also observe that simple learning models on top of DM++ can provide more challenging baselines. We finally introduce embedding-based methodologies to perform a) vocabulary expansion to address data scarcity and b) vocabulary porting to new languages in case training data is not available.

In this work we present a system that generates reports for a game of Risk. These reports, far from being “neutral”, aim instead at mimicking propaganda, and try to influence the opinions of the readers about the performance of a Risk player. The system, while limited in scope, was able to persuade some test subjects in a qualitative evaluation, hinting at the abilities that a more sophisticated system might have.

BACKGROUND: Current alignment tools typically lack an explicit model of indel evolution, leading to artificially short inferred alignments (i.e., over-alignment) due to inconsistencies between the indel history and the phylogeny relating the input sequences. RESULTS: We present a new progressive multiple sequence alignment tool ProPIP. The process of insertions and deletions is described using an explicit evolutionary model-the Poisson Indel Process or PIP. The method is based on dynamic programming and is implemented in a frequentist framework. The source code can be compiled on Linux, macOS and Microsoft Windows platforms. The algorithm is implemented in C++ as standalone program. The source code is freely available on GitHub at https://github.com/acg-team/ProPIP and is distributed under the terms of the GNU GPL v3 license. CONCLUSIONS: The use of an explicit indel evolution model allows to avoid over-alignment, to infer gaps in a phylogenetically consistent way and to make inferences about the rates of insertions and deletions. Instead of the arbitrary gap penalties, the parameters used by ProPIP are the insertion and deletion rates, which have biological interpretation and are contextualized in a probabilistic environment. As a result, indel rate settings may be optimised in order to infer phylogenetically meaningful gap patterns.

This paper presents a new version of a football reports generation system called PASS. The original version generatedDutch text and relied on a limited handcrafted knowledge base.We describe how, in a short amount of time, we extended PASS to produce English texts, exploiting machine translation and Wikidata as a large-scale source of multilingual knowledge.

Altering the lyrics of famous songs is a common creative and communicative act, often used for purposes that go beyond simple amusement, such as the creation of companion music for advertisements. In this case, the altered song commonly refers to the advertised product or idea. Here we present a system that can automatically reproduce this process: it starts from a novel text, i.e. the daily news, identifies the key concepts therein contained, expands them and then uses this word cloud to replace some of the words in a song, obeying lexical, metrical and rhyming constraints. The new song is then created by merging these new lyrics, sung by a speech synthesizer, with the original backing music. Our evaluation shows that songs created by the system increase the recall of the news they were created from.

We present a methodology for building lexical sets for argument slots of Italian verbs.We start from an inventory of semantically typed Italian verb frames and through a mapping to WordNet we automatically annotate the sets of fillers for the argument positions in a corpus of sentences.We evaluate both a baseline algorithm and a syntax driven algorithm and show that the latter performs significantly better in terms of precision.

Deriving prior polarity lexica for sentiment analysis - where positive or negative scores are associated with words out of context - is a challenging task. Usually, a trade-off between precision and coverage is hard to find, and it depends on the methodology used to build the lexicon. Manually annotated lexica provide a high precision but lack in coverage, whereas automatic derivation from pre-existing knowledge guarantees high coverage at the cost of a lower precision. Since the automatic derivation of prior polarities is less time consuming than manual annotation, there has been a great bloom of these approaches, in particular based on the SentiWordNet resource. In this paper, we compare the most frequently used techniques based on SentiWordNet with newer ones and blend them in a learning framework (a so called `ensemble method'). By taking advantage of manually built prior polarity lexica, our ensemble method is better able to predict the prior value of unseen words and to outperform all the other SentiWordNet approaches. Using this technique we have built SentiWords, a prior polarity lexicon of approximately 155,000 words, that has both a high precision and a high coverage. We finally show that in sentiment analysis tasks, using our lexicon allows us to outperform both the single metrics derived from SentiWordNet and popular manually annotated sentiment lexica.

The need for creativity is ubiquitous, and mobile devices connected to Web services can help us. Linguistic creativity is widely used in advertisements to surprise us, to get our attention, and to stick concepts in our memory. However, creativity can also be used as a defense. When we walk in the street, we are overwhelmed by messages that try to get our attention with any persuasive device at hand. As messages get ever more aggressive, often our basic cognitive defenses—trying not to perceive those messages—are not sufficient. One advanced defensive technique is based on transforming the perceived message into something different (for instance, making use of irony or hyperbole) from what was originally meant in the message. In this article, we describe an implemented application for smartphones, which creatively modifies the linguistic expression in a virtual copy of a poster encountered on the street. The mobile system is inspired by thesubvertisingpractice of countercultural art.

Assigning a positive or negative score to a word out of context (i.e. a word's prior polarity) is a challenging task for sentiment analysis.In the literature, various approaches based on SentiWordNet have been proposed.In this paper, we compare the most often used techniques together with newly proposed ones and incorporate all of them in a learning framework to see whether blending them can further improve the estimation of prior polarity scores.Using two different versions of Sen-tiWordNet and testing regression and classification models across tasks and datasets, our learning approach consistently outperforms the single metrics, providing a new state-ofthe-art approach in computing words' prior polarity for sentiment analysis.We conclude our investigation showing interesting biases in calculated prior polarity scores when word Part of Speech and annotator gender are considered.