Towards Multi-Modal Interactions in Virtual Environments: A Case Study

We present research on visualization and interaction in a realistic model of an existing theatre. This existing ‘Muziek¬centrum’ offers its visitors information about performances by means of a yearly brochure. In addition, it is possible to get information at an information desk in the theatre (during office hours), to get information by phone (by talking to a human or by using IVR). The database of the theater holds the information that is available at the beginning of the ‘theatre season’. Our aim is to make this information more accessible by using multi-modal accessible multi-media web pages. A more general aim is to do research in the area of web-based services, in particu¬lar interactions in virtual environments

Design and Development of an Extensible and Configurable Framework for Conversational Search Experiments

The Conversational Search (CS) paradigm allows for an intuitive interaction between the user and the system through natural language sentences and it is increasingly being adopted in various scenarios. However, its widespread experimentation has led to the birth of a multitude of CS systems with custom implementations and variants of Information Retrieval (IR) models. This exacerbates the reproducibility crisis already observed in several research areas, including IR. To address this issue, we propose DECAF: a modular and extensible Conversational Search framework designed for fast prototyping and development of conversational agents. Our framework integrates all the components that characterize a modern CS system and allows for the seamless integration of Machine Learning (ML) and Large Language Models (LLMs)-based techniques. Furthermore, thanks to its uniform interface, DECAF allows for experiments characterized by a high degree of reproducibility. DECAF contains several state-of-the-art components including query rewriting, search functions under Bag-of-Words (BoW) and dense paradigms, and re-ranking functions. Our framework is tested on two well-known conversational collections: TREC CAsT 2019 and 2020 and the results can be used by future practitioners as baselines. Our contributions include the identification of a series of state-of-the-art components for the CS task and the definition of a modular framework for its implementation.The Conversational Search (CS) paradigm allows for an intuitive interaction between the user and the system through natural language sentences and it is increasingly being adopted in various scenarios. However, its widespread experimentation has led to the birth of a multitude of CS systems with custom implementations and variants of Information Retrieval (IR) models. This exacerbates the reproducibility crisis already observed in several research areas, including IR. To address this issue, we propose DECAF: a modular and extensible Conversational Search framework designed for fast prototyping and development of conversational agents. Our framework integrates all the components that characterize a modern CS system and allows for the seamless integration of Machine Learning (ML) and Large Language Models (LLMs)-based techniques. Furthermore, thanks to its uniform interface, DECAF allows for experiments characterized by a high degree of reproducibility. DECAF contains several state-of-the-art components including query rewriting, search functions under Bag-of-Words (BoW) and dense paradigms, and re-ranking functions. Our framework is tested on two well-known conversational collections: TREC CAsT 2019 and 2020 and the results can be used by future practitioners as baselines. Our contributions include the identification of a series of state-of-the-art components for the CS task and the definition of a modular framework for its implementation

SARG: A Novel Semi Autoregressive Generator for Multi-turn Incomplete Utterance Restoration

Dialogue systems in open domain have achieved great success due to the easily obtained single-turn corpus and the development of deep learning, but the multi-turn scenario is still a challenge because of the frequent coreference and information omission. In this paper, we investigate the incomplete utterance restoration which has brought general improvement over multi-turn dialogue systems in recent studies. Meanwhile, jointly inspired by the autoregression for text generation and the sequence labeling for text editing, we propose a novel semi autoregressive generator (SARG) with the high efficiency and flexibility. Moreover, experiments on two benchmarks show that our proposed model significantly outperforms the state-of-the-art models in terms of quality and inference speed.Comment: Accepted to AAAI 202