A Sequence-to-Sequence Model for User Simulation in Spoken Dialogue Systems
User simulation is essential for generating enough data to train a statistical spoken dialogue system. Previous models for user simulation suffer from several drawbacks, such as the inability to take dialogue history into account, the need of rigid structure to ensure coherent user behaviour, heavy dependence on a specific domain, the inability to output several user intentions during one dialogue turn, or the requirement of a summarized action space for tractability. This paper introduces a data-driven user simulator based on an encoder-decoder recurrent neural network. The model takes as input a sequence of dialogue contexts and outputs a sequence of dialogue acts corresponding to user intentions. The dialogue contexts include information about the machine acts and the status of the user goal. We show on the Dialogue State Tracking Challenge 2 (DSTC2) dataset that the sequence-to-sequence model outperforms an agenda-based simulator and an n-gram simulator, according to F-score. Furthermore, we show how this model can be used on the original action space and thereby models user behaviour with finer granularity.
Code (0)
등록된 구현이 없습니다.
Tasks
DecoderDialogue State TrackingSpoken Dialogue SystemsUser SimulationSimilar Papers 제목 키워드 기반
SLIDE: Integrating Speech Language Model with LLM for Spontaneous Spoken Dialogue Generation
Recently, ``textless" speech language models (SLMs) based on speech units have made huge progress in generating naturalistic speech, including non-verbal vocalizations. However, the generated speech samples often lack se…
Dialogue GenerationLanguage ModelingLanguage ModellingA Context-aware Natural Language Generator for Dialogue Systems
We present a novel natural language generation system for spoken dialogue systems capable of entraining (adapting) to users' way of speaking, providing contextually appropriate responses. The generator is based on recurr…
Spoken Dialogue SystemsText GenerationSequence-to-Sequence Generation for Spoken Dialogue via Deep Syntax Trees and Strings
Learning from Real Users: Rating Dialogue Success with Neural Networks for Reinforcement Learning in Spoken Dialogue Systems
To train a statistical spoken dialogue system (SDS) it is essential that an accurate method for measuring task success is available. To date training has relied on presenting a task to either simulated or paid users and …
Reinforcement LearningSpoken Dialogue SystemsPSLM: Parallel Generation of Text and Speech with LLMs for Low-Latency Spoken Dialogue Systems
Multimodal language models that process both text and speech have a potential for applications in spoken dialogue systems. However, current models face two major challenges in response generation latency: (1) generating …
Language ModelingLanguage ModellingQuestion AnsweringResponse Generation+1