Budgeted Policy Learning for Task-Oriented Dialogue Systems

This paper presents a new approach that extends Deep Dyna-Q (DDQ) by incorporating a Budget-Conscious Scheduling (BCS) to best utilize a fixed, small amount of user interactions (budget) for learning task-oriented dialogue agents. BCS consists of (1) a Poisson-based global scheduler to allocate budget over different stages of training; (2) a controller to decide at each training step whether the agent is trained using real or simulated experiences; (3) a user goal sampling module to generate the experiences that are most effective for policy learning. Experiments on a movie-ticket booking task with simulated and real users show that our approach leads to significant improvements in success rate over the state-of-the-art baselines given the fixed budget.Comment: 10 pages, 7 figures, ACL 201

Improving Dialogue Management: Quality Datasets vs Models

Task-oriented dialogue systems (TODS) have become crucial for users to interact with machines and computers using natural language. One of its key components is the dialogue manager, which guides the conversation towards a good goal for the user by providing the best possible response. Previous works have proposed rule-based systems (RBS), reinforcement learning (RL), and supervised learning (SL) as solutions for the correct dialogue management; in other words, select the best response given input by the user. However, this work argues that the leading cause of DMs not achieving maximum performance resides in the quality of the datasets rather than the models employed thus far; this means that dataset errors, like mislabeling, originate a large percentage of failures in dialogue management. We studied the main errors in the most widely used datasets, Multiwoz 2.1 and SGD, to demonstrate this hypothesis. To do this, we have designed a synthetic dialogue generator to fully control the amount and type of errors introduced in the dataset. Using this generator, we demonstrated that errors in the datasets contribute proportionally to the performance of the model

Minimising medicine use in organic dairy herds through animal health and welfare planning

Livestock is important in many organic farming systems, and it is an explicit goal to ensure high levels of animal health and welfare (AHW) through good management. This will lead to reduced medicine use and better quality of animal products. In two EU network projects NAHWOA & SAFO it was concluded that this is not guaranteed merely by following organic standards. Both networks recommended implementation of individual animal health plans to stimulate organic farmers to improve AHW. These plans should include a systematic evaluation of AHW and be implemented through dialogue with each farmer in order to identify goals and plan improvements. 15 research institutions in 8 European countries are involved in the proposed project with the main objective to minimise medicine use in organic dairy herds through active and well planned AHW promotion and disease prevention. The project consists of 5 work packages, 4 of which comprise research activities building on current research projects, new applications across borders, exchange of knowledge, results and conclusions between participating countries, and adopting them to widely different contexts. International and national workshops facilitate this exchange. Focus areas are animal health planning, AHW assessment using animal based parameters and development of advisory systems and farmer groups. Epidemiological analyses of the effect on AHW from reduced medicine use and herd improvements are planned in all participating countries

FREDSum: A Dialogue Summarization Corpus for French Political Debates

Recent advances in deep learning, and especially the invention of encoder-decoder architectures, has significantly improved the performance of abstractive summarization systems. The majority of research has focused on written documents, however, neglecting the problem of multi-party dialogue summarization. In this paper, we present a dataset of French political debates for the purpose of enhancing resources for multi-lingual dialogue summarization. Our dataset consists of manually transcribed and annotated political debates, covering a range of topics and perspectives. We highlight the importance of high quality transcription and annotations for training accurate and effective dialogue summarization models, and emphasize the need for multilingual resources to support dialogue summarization in non-English languages. We also provide baseline experiments using state-of-the-art methods, and encourage further research in this area to advance the field of dialogue summarization. Our dataset will be made publicly available for use by the research community.Comment: Accepted at EMNLP2023 Finding