This paper introduces the shared task of summarizing documents in several creative domains, namely literary texts, movie scripts, and television scripts. Summarizing these creative documents requires making complex literary interpretations, as well as understanding non-trivial temporal dependencies in texts containing varied styles of plot development and narrative structure. This poses unique challenges and is yet underexplored for text summarization systems. In this shared task, we introduce four sub-tasks and their corresponding datasets, focusing on summarizing books, movie scripts, primetime television scripts, and daytime soap opera scripts. We detail the process of curating these datasets for the task, as well as the metrics used for the evaluation of the submissions. As part of the CREATIVESUMM workshop at COLING 2022, the shared task attracted 18 submissions in total. We discuss the submissions and the baselines for each sub-task in this paper, along with directions for facilitating future work in the field.

In recent years, unmanned aerial vehicle (UAV) related technology has expanded knowledge in the area, bringing to light new problems and challenges that require solutions. Furthermore, because the technology allows processes usually carried out by people to be automated, it is in great demand in industrial sectors. The automation of these vehicles has been addressed in the literature, applying different machine learning strategies. Reinforcement learning (RL) is an automation framework that is frequently used to train autonomous agents. RL is a machine learning paradigm wherein an agent interacts with an environment to solve a given task. However, learning autonomously can be time consuming, computationally expensive, and may not be practical in highly-complex scenarios. Interactive reinforcement learning allows an external trainer to provide advice to an agent while it is learning a task. In this study, we set out to teach an RL agent to control a drone using reward-shaping and policy-shaping techniques simultaneously. Two simulated scenarios were proposed for the training; one without obstacles and one with obstacles. We also studied the influence of each technique. The results show that an agent trained simultaneously with both techniques obtains a lower reward than an agent trained using only a policy-based approach. Nevertheless, the agent achieves lower execution times and less dispersion during training.

Our network is trained using triplets of songs such that two songs by the same In this paper, we explore the feasibility of using Triplet artist are embedded closer to one another than to networks, a variant of Siamese networks (Bromley et al., a third song by a different artist. We compare 1994), for content-based music recommendation. In this two models that are trained using different ways context, a Triplet network learns an embedding of an item of picking this third song: at random vs. based such that the item is close to other similar items and far on shared genre labels. Our experiments are conducted from dissimilar items in the embedding space. To train using songs from the Free Music Archive the network, we will consider songs by the same artist to and use standard audio features. The initial results be similar and songs by all other artists to be dissimilar.

Controlled text generation is a very important task in the arena of natural language processing due to its promising applications. In order to achieve this task we mainly introduce the novel soft prompt tuning method of using soft prompts at both encoder and decoder levels together in a T5 model and investigate the performance as the behaviour of an additional soft prompt related to the decoder of a T5 model in controlled text generation remained unexplored. Then we also investigate the feasibility of steering the output of this extended soft prompted T5 model at decoder level and finally analyse the utility of generated text to be used in AI related tasks such as training AI models with an interpretability analysis of the classifier trained with synthetic text, as there is a lack of proper analysis of methodologies in generating properly labelled data to be utilized in AI tasks. Through the performed in-depth intrinsic and extrinsic evaluations of this generation model along with the artificially generated data, we found that this model produced better results compared to the T5 model with a single soft prompt at encoder level and the sentiment classifier trained using this artificially generated data can produce comparable classification results to the results of a classifier trained with real labelled data and also the classifier decision is interpretable with respect to the input text content.

Any organization needs to improve their products, services, and processes. In this context, engaging with customers and understanding their journey is essential. Organizations have leveraged various techniques and technologies to support customer engagement, from call centres to chatbots and virtual agents. Recently, these systems have used Machine Learning (ML) and Natural Language Processing (NLP) to analyze large volumes of customer feedback and engagement data. The goal is to understand customers in context and provide meaningful answers across various channels. Despite multiple advances in Conversational Artificial Intelligence (AI) and Recommender Systems (RS), it is still challenging to understand the intent behind customer questions during the customer journey. To address this challenge, in this paper, we study and analyze the recent work in Conversational Recommender Systems (CRS) in general and, more specifically, in chatbot-based CRS. We introduce a pipeline to contextualize the input utterances in conversations. We then take the next step towards leveraging reverse feature engineering to link the contextualized input and learning model to support intent recognition. Since performance evaluation is achieved based on different ML models, we use transformer base models to evaluate the proposed approach using a labelled dialogue dataset (MSDialogue) of question-answering interactions between information seekers and answer providers.

We study the capabilities of speech processing systems trained simply to predict large amounts of transcripts of audio on the internet. When scaled to 680,000 hours of multilingual and multitask supervision, the resulting models generalize well to standard benchmarks and are often competitive with prior fully supervised results but in a zero-shot transfer setting without the need for any fine-tuning. When compared to humans, the models approach their accuracy and robustness. We are releasing models and inference code to serve as a foundation for further work on robust speech processing.

Professors, programmers and journalists could all be out of a job in just a few years, after the latest chatbot from the Elon Musk-founded OpenAI foundation stunned onlookers with its writing ability, proficiency at complex tasks, and ease of use. The system, called ChatGPT, is the latest evolution of the GPT family of text-generating AIs. Two years ago, the team's previous AI, GPT3, was able to generate an opinion piece for the Guardian, and ChatGPT has significant further capabilities. In the days since it was released, academics have generated responses to exam queries that they say would result in full marks if submitted by an undergraduate, and programmers have used the tool to solve coding challenges in obscure programming languages in a matter of seconds – before writing limericks explaining the functionality. Dan Gillmor, a journalism professor at Arizona State University, asked the AI to handle one of the assignments he gives his students: writing a letter to a relative giving advice regarding online security and privacy.

ChatGPT is a prototype dialogue-based AI chatbot capable of understanding natural human language and generating impressively detailed human-like written text. It is the latest evolution of the GPT – or Generative Pre-Trained Transformer – family of text-generating AIs. The new AI is the latest chatbot from the Elon Musk-founded independent research body OpenAI foundation. Musk co-founded the startup with other Silicon Valley investors including technology venture capitalist Sam Altman in late 2015, saying that the research centre would "advance digital intelligence in the way that is most likely to benefit humanity" according to a blog post at the time. The Twitter CEO has since left the board and distanced himself from the company, tweeting on Sunday that after he "learned" that OpenAI was accessing the platform's database for "training", he put a pause on it.

Disney has hopped into the realm of being play with the knob of time, as the company has developed a new AI tool that is capable of winding back the clock for actors. The new artificial intelligence tool is called the Face Re-aging Network (FRAN), and is capable of automatically changing the age of actors, which will undoubtedly speed up the visual effects editing process that already takes several months to days, depending on the length of the content being altered. Manual de-aging typically involves an individual going through every single frame of the film and painting the appropriate effect onto the actor's skin. Another way is completely replacing the actor with a digital puppet to speed up the editing process. Now, Disney plans on putting the majority of that heavy lifting onto the shoulders of an AI, specifically FRAN, that the company says already complements traditional re-aging techniques that are already widely used in film production.

Many people see artificial intelligence as an efficient but unimaginative tool that performs routine tasks. Analysing datasets or checking financial reports is one thing. But AI is fast becoming skilled at bringing together ideas, information, and artistic influences to generate creative work. Language-learning models can mimic human imagination and write articles like an experienced journalist or lawyer. AI might also replace conventional search engines by providing one-line responses summarising years' worth of scientific research, The Atlantic reports. Meanwhile, non-profit OpenAI unveiled a new chatbot, called ChatGPT.