We present ConvLab-2, an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models, perform an end-to-end evaluation, and diagnose the weakness of systems. As the successor of ConvLab (Lee et al., 2019b), ConvLab-2 inherits ConvLab's framework but integrates more powerful dialogue models and supports more datasets. Besides, we have developed an analysis tool and an interactive tool to assist researchers in diagnosing dialogue systems. The analysis tool presents rich statistics and summarizes common mistakes from simulated dialogues, which facilitates error analysis and system improvement. The interactive tool provides a user interface that allows developers to diagnose an assembled dialogue system by interacting with the system and modifying the output of each system component.
The Dialogue on Dialogues workshop was organized as a satellite event at the Interspeech 2006 conference in Pittsburgh, Pennsylvania, and it was held on September 17, 2006, immediately before the main conference. It was planned and coordinated by Michael McTear (University of Ulster, UK), Kristiina Jokinen (University of Helsinki, Finland), and James A. Larson (Portland State University, USA). The one-day workshop involved more than 40 participants from Europe, the United States, Australia, and Japan. One of the motivations for furthering the systems' interaction capabilities is to improve the AI Magazine Volume 28 Number 2 (2007) ( AAAI) However, relatively little work has so far been devoted to defining the criteria according to which we could evaluate such systems in terms of increased naturalness and usability. It is often felt that statistical speech-based research is not fully appreciated in the dialogue community, while dialogue modeling in the speech community seems too simple in terms of the advanced architectures and functionalities under investigation in the dialogue community.
The Dialogue on Dialogues workshop was organized as a satellite event at the Interspeech 2006 conference in Pittsburgh, Pennsylvania, and it was held on September 17, 2006, immediately before the main conference. It was planned and coordinated by Michael McTear (University of Ulster, UK), Kristiina Jokinen (University of Helsinki, Finland), and James A. Larson (Portland State University, USA). The one-day workshop involved more than 40 participants from Europe, the United States, Australia, and Japan.
Dialogue assistants are rapidly becoming an indispensable daily aid. To avoid the significant effort needed to hand-craft the required dialogue flow, the Dialogue Management (DM) module can be cast as a continuous Markov Decision Process (MDP) and trained through Reinforcement Learning (RL). Several RL models have been investigated over recent years. However, the lack of a common benchmarking framework makes it difficult to perform a fair comparison between different models and their capability to generalise to different environments. Therefore, this paper proposes a set of challenging simulated environments for dialogue model development and evaluation. To provide some baselines, we investigate a number of representative parametric algorithms, namely deep reinforcement learning algorithms - DQN, A2C and Natural Actor-Critic and compare them to a non-parametric model, GP-SARSA. Both the environments and policy models are implemented using the publicly available PyDial toolkit and released on-line, in order to establish a testbed framework for further experiments and to facilitate experimental reproducibility.
Argumentation-based dialogue systems, which can handle and exchange arguments through dialogue, have been widely researched. It is required that these systems have sufficient supporting information to argue their claims rationally; however, the systems often do not have enough of such information in realistic situations. One way to fill in the gap is acquiring such missing information from dialogue partners (information-seeking dialogue). Existing information-seeking dialogue systems are based on handcrafted dialogue strategies that exhaustively examine missing information. However, the proposed strategies are not specialized in collecting information for constructing rational arguments. Moreover, the number of system's inquiry candidates grows in accordance with the size of the argument set that the system deal with. In this paper, we formalize the process of information-seeking dialogue as Markov decision processes (MDPs) and apply deep reinforcement learning (DRL) for automatically optimizing a dialogue strategy. By utilizing DRL, our dialogue strategy can successfully minimize objective functions, the number of turns it takes for our system to collect necessary information in a dialogue. We conducted dialogue experiments using two datasets from different domains of argumentative dialogue. Experimental results show that the proposed formalization based on MDP works well, and the policy optimized by DRL outperformed existing heuristic dialogue strategies.