In this work, we study dialogue scenarios that start from chit-chat but eventually switch to task-related services, and investigate how a unified dialogue model, which can engage in both chit-chat and task-oriented dialogues, takes the initiative during the dialogue mode transition from chit-chat to task-oriented in a coherent and cooperative manner.We firstly build a transition info extractor (TIE) that keeps track of the preceding chit-chat interaction and detects the potential user intention to switch to a taskoriented service.Meanwhile, in the unified model, a transition sentence generator (TSG) is extended through efficient Adapter tuning and transition prompt learning.When the TIE successfully finds task-related information from the preceding chit-chat, such as a transition domain ("train" in Figure 1), then the TSG is activated automatically in the unified model to initiate this transition by generating a transition sentence under the guidance of transition information extracted by TIE.The experimental results show promising performance regarding the proactive transitions.We achieve an additional large improvement on TIE model by utilizing Conditional Random Fields (CRF).The TSG can flexibly generate transition sentences while maintaining the unified capabilities of normal chit-chat and task-oriented response generation.

Recently, principal reward components for dialogue policy reinforcement learning use task success and user satisfaction independently and neither the resulting learned behaviour has been analysed nor a suitable proper analysis method even existed.In this work, we employ both principal reward components jointly and propose a method to analyse the resulting behaviour through a structured way of probing the learned policy.We show that blending both reward components increases user satisfaction without sacrificing task success even in more hostile environments and provide insight about actions chosen by the learned policies.

The differences in decision making between behavioural models of voice interfaces are hard to capture using existing measures for the absolute performance of such models.For instance, two models may have a similar task success rate, but very different ways of getting there.In this paper, we propose a general methodology to compute the similarity of two dialogue behaviour models and investigate different ways of computing scores on both the semantic and the textual level.Complementing absolute measures of performance, we test our scores on three different tasks and show the practical usability of the measures.

Recent spoken dialog systems are moving away from command and control towards a more intuitive and natural style of interaction. In order to choose an appropriate system design which allows the system to deal with naturally spoken user input, a definition of what exactly constitutes naturalness in user input is important. In this paper, we examine how different user groups naturally speak to an automotive spoken dialog system (SDS). We conduct a user study in which we collect freely spoken user utterances for a wide range of use cases in German. By means of a comparative study of the utterances from the study with interpersonal utterances, we provide criteria what constitutes naturalness in the user input of an state-of-the-art automotive SDS.

Wolfgang Maier. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.

LR parsing is a popular parsing strategy for variants of Context-Free Grammar (CFG).It has also been used for mildly context-sensitive formalisms, such as Tree-Adjoining Grammar.In this paper, we present the first LRstyle parsing algorithm for Linear Context-Free Rewriting Systems (LCFRS), a mildly context-sensitive extension of CFG which has received considerable attention in the last years.

This paper presents the first efficient implementation of a weighted deductive CYK parser for Probabilistic Linear Context-Free Rewriting Systems (PLCFRSs). LCFRS, an extension of CFG, can describe discontinuities in a straightforward way and is therefore a natural candidate to be used for data-driven parsing. To speed up parsing, we use different context-summary estimates of parse items, some of them allowing for A* parsing. We evaluate our parser with grammars extracted from the German NeGra treebank. Our experiments show that data-driven LCFRS parsing is feasible and yields output of competitive quality.