Reinforcement Learning and Bandits for Speech and Language Processing: Tutorial, Review and Outlook

In recent years, reinforcement learning and bandits have transformed a wide range of real-world applications including healthcare, finance, recommendation systems, robotics, and last but not least, the speech and natural language processing. While most speech and language applications of reinforcement learning algorithms are centered around improving the training of deep neural networks with its flexible optimization properties, there are still many grounds to explore to utilize the benefits of reinforcement learning, such as its reward-driven adaptability, state representations, temporal structures and generalizability. In this survey, we present an overview of recent advancements of reinforcement learning and bandits, and discuss how they can be effectively employed to solve speech and natural language processing problems with models that are adaptive, interactive and scalable.Comment: To appear in Expert Systems with Applications. Accompanying INTERSPEECH 2022 Tutorial on the same topic. Including latest advancements in large language models (LLMs

Transfer Learning for Sequence Labeling Using Source Model and Target Data

In this paper, we propose an approach for transferring the knowledge of a neural model for sequence labeling, learned from the source domain, to a new model trained on a target domain, where new label categories appear. Our transfer learning (TL) techniques enable to adapt the source model using the target data and new categories, without accessing to the source data. Our solution consists in adding new neurons in the output layer of the target model and transferring parameters from the source model, which are then fine-tuned with the target data. Additionally, we propose a neural adapter to learn the difference between the source and the target label distribution, which provides additional important information to the target model. Our experiments on Named Entity Recognition show that (i) the learned knowledge in the source model can be effectively transferred when the target data contains new categories and (ii) our neural adapter further improves such transfer.Comment: 9 pages, 4 figures, 3 tables, accepted paper in the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19

Mobile language learning now and in the future

The widespread ownership of mobile devices such as cellphones, personal media players, personal digital assistants (PDAs), smartphones and wireless laptops means that ‘mobile learning’ is no longer in the preserve of technical experts and researchers with specialist knowledge. Teachers and learners have begun to integrate mobile technologies into everyday practices and there is evidence of efforts to invent exciting new scenarios of use. Language learning is one of the disciplines that looks set to benefit from these developments. Learners can make good use of the facilities to record and to listen to audio at any time, supported by the rising availability of podcasts and the ‘always on’ characteristics of portable devices which encourage spontaneous interactions. Mobile learning promises to deliver closer integration of language learning with everyday communication needs and cultural experiences

English syllabus for students of 3rd year of Primary Education

The book deals with all the general elements that must be considered when designing a syllabus including the legal framework, the teaching context and a justification of the design. It includes the general and specific objectives, basic competences, contents, methodology, evaluation, attention to diversity, the fourteen didactic units, as well as a comprehensive bibliography and a final conclusion

Teaching Programme: Cinema and TV

En este trabajo, se presenta una programación didáctica centrada en el curso de tercero de Educación Secundaria. Haremos un análisis de objetivos, contenidos y competencias que se centran en el tema del cine y la televisión, que es la unidad didáctica dónde nos vamos a centrar. Además se incluyen los instrumentos y criterios de evaluación y una secuencia de actividades tipo, centrada en la enseñanza dinámica y por un enfoque basado en tareas.In this work, a didactic program is presented focused on the third course of Secondary Education. We will do an analysis of objectives, contents and skills that focus on the theme of film and television, which is the didactic unit in which we are going to focus. In addition to including evaluation tools and criteria and a sequence of type of activities, focused on dynamic teaching and a task-based approach

Text Style Transfer: A Review and Experimental Evaluation

The stylistic properties of text have intrigued computational linguistics researchers in recent years. Specifically, researchers have investigated the Text Style Transfer (TST) task, which aims to change the stylistic properties of the text while retaining its style independent content. Over the last few years, many novel TST algorithms have been developed, while the industry has leveraged these algorithms to enable exciting TST applications. The field of TST research has burgeoned because of this symbiosis. This article aims to provide a comprehensive review of recent research efforts on text style transfer. More concretely, we create a taxonomy to organize the TST models and provide a comprehensive summary of the state of the art. We review the existing evaluation methodologies for TST tasks and conduct a large-scale reproducibility study where we experimentally benchmark 19 state-of-the-art TST algorithms on two publicly available datasets. Finally, we expand on current trends and provide new perspectives on the new and exciting developments in the TST field

A Reference Model for Collaborative Business Intelligence Virtual Assistants

Collaborative Business Analysis (CBA) is a methodology that involves bringing together different stakeholders, including business users, analysts, and technical specialists, to collaboratively analyze data and gain insights into business operations. The primary objective of CBA is to encourage knowledge sharing and collaboration between the different groups involved in business analysis, as this can lead to a more comprehensive understanding of the data and better decision-making. CBA typically involves a range of activities, including data gathering and analysis, brainstorming, problem-solving, decision-making and knowledge sharing. These activities may take place through various channels, such as in-person meetings, virtual collaboration tools or online forums. This paper deals with virtual collaboration tools as an important part of Business Intelligence (BI) platform. Collaborative Business Intelligence (CBI) tools are becoming more user-friendly, accessible, and flexible, allowing users to customize their experience and adapt to their specific needs. The goal of a virtual assistant is to make data exploration more accessible to a wider range of users and to reduce the time and effort required for data analysis. It describes the unified business intelligence semantic model, coupled with a data warehouse and collaborative unit to employ data mining technology. Moreover, we propose a virtual assistant for CBI and a reference model of virtual tools for CBI, which consists of three components: conversational, data exploration and recommendation agents. We believe that the allocation of these three functional tasks allows you to structure the CBI issue and apply relevant and productive models for human-like dialogue, text-to-command transferring, and recommendations simultaneously. The complex approach based on these three points gives the basis for virtual tool for collaboration. CBI encourages people, processes, and technology to enable everyone sharing and leveraging collective expertise, knowledge and data to gain valuable insights for making better decisions. This allows to respond more quickly and effectively to changes in the market or internal operations and improve the progress