Communicative Intentions Annotation Scheme for Natural Language Processing Applications

Communicative intentions are one of the linguistic elements that usually determine the content of any message we want to express in our social interactions. With the purpose of contributing to the improvement of natural language processing systems, this thesis aims to create a communicative intention annotation scheme based on the taxonomy presented in the Speech Act Theory. In this way, language processing tools could consider communicative intentions as a starting point to help classify any message and its content depending first on the intention it reflects. To do so, the scheme will be created with the help of an already annotated corpus of Spanish tweets and subsequently evaluated by external annotators so that we can confirm the appropriateness and reliability of the tagged intentions before applying the scheme to an NLP system. Thus, it will be possible to check up to which point communicative intentions can improve the identification of the purpose of a message in an already created NLP system so that we can gain more linguistic information from any text automatically.This research work is part of the R&D project "PID2021-123956OB-I00", funded by MCIN/AEI/10.13039/501100011033/ and by "ERDF A way of making Europe”. Moreover, it has been partially funded by the Generalitat Valenciana through the project NL4DISMIS: Natural Language Technologies for dealing with dis- and misinformation with grant reference (CIPROM/2021/21)"

Communicative Intentions Annotation Scheme for Natural Language Generation

Communicative intentions are one of the linguistic elements that usually determine the content of any text or message we want to express in our communicative interactions. With the purpose of contributing to the improvement of natural language generation systems, so that they can take the communicative intention as one of the starting points that will determine the structure and content of the message generated, the aim of this project is to create a communicative intentions annotation scheme based on the taxonomy presented in the Speech Act Theory. To do so, the scheme will be created with the help of a linguistic corpus and subsequently tested within a natural language generation system. In this way, it will be possible to check up to which point communicative intentions improve the planning stage of the text to be generated automatically, guiding the rest of decisions to be made by the system in order to create automatic messages with more similar results to any manually created text.This research work has been funded by the University of Alicante (Spain) and the Ministry of Economic Affairs and Digital Transformation of the Spanish Government through the project INTEGER (RTI2018-094649-B-I00)

Transformer Neural Networks for Automated Story Generation

Towards the last two-decade Artificial Intelligence (AI) proved its use on tasks such as image recognition, natural language processing, automated driving. As discussed in the Moore’s law the computational power increased rapidly over the few decades (Moore, 1965) and made it possible to use the techniques which were computationally expensive. These techniques include Deep Learning (DL) changed the field of AI and outperformed other models in a lot of fields some of which mentioned above. However, in natural language generation especially for creative tasks that needs the artificial intelligent models to have not only a precise understanding of the given input, but an ability to be creative, fluent and, coherent within a content. One of these tasks is automated story generation which has been an open research area from the early days of artificial intelligence. This study investigates whether the transformer network can outperform state-of-the-art model for automated story generation. A large dataset gathered from Reddit’s WRITING PROMPTS sub forum and processed by the transformer network in order to compare the perplexity and two human evaluation metrics on transformer network and the state-of-the-art model. It was found that the transformer network cannot outperform the state-of-art model and even though it generated viable and novel stories it didn’t pay much attention to the prompts of the generated stories. Also, the results implied that there should be a better automated evaluation metric in order to assess the performance of story generation models