52 research outputs found
User Satisfaction Reward Estimation Across Domains: Domain-independent Dialogue Policy Learning
Learning suitable and well-performing dialogue behaviour in statistical spoken dialogue systems has been in the focus of research for many years. While most work that is based on reinforcement learning employs an objective measure like task success for modelling the reward signal, we propose to use a reward signal based on user satisfaction. We propose a novel estimator and show that it outperforms all previous estimators while learning temporal dependencies implicitly. We show in simulated experiments that a live user satisfaction estimation model may be applied resulting in higher estimated satisfaction whilst achieving similar success rates. Moreover, we show that a satisfaction estimation model trained on one domain may be applied in many other domains that cover a similar task. We verify our findings by employing the model to one of the domains for learning a policy from real users and compare its performance to policies using user satisfaction and task success acquired directly from the users as reward
When to Say What and How: Adapting the Elaborateness and Indirectness of Spoken Dialogue Systems
With the aim of designing a spoken dialogue system which has the ability to adapt to the user's communication idiosyncrasies, we investigate whether it is possible to carry over insights from the usage of communication styles in human-human interaction to human-computer interaction. In an extensive literature review, it is demonstrated that communication styles play an important role in human communication. Using a multi-lingual data set, we show that there is a significant correlation between the communication style of the system and the preceding communication style of the user. This is why two components that extend the standard architecture of spoken dialogue systems are presented: 1) a communication style classifier that automatically identifies the user communication style and 2) a communication style selection module that selects an appropriate system communication style. We consider the communication styles elaborateness and indirectness as it has been shown that they influence the user's satisfaction and the user's perception of a dialogue. We present a neural classification approach based on supervised learning for each task. Neural networks are trained and evaluated with features that can be automatically derived during an ongoing interaction in every spoken dialogue system. It is shown that both components yield solid results and outperform the baseline in form of a majority-class classifier
ConceptNet infused DialoGPT for Underlying Commonsense Understanding and Reasoning in Dialogue Response Generation
The pre-trained conversational models still fail to capture the implicit
commonsense (CS) knowledge hidden in the dialogue interaction, even though they
were pre-trained with an enormous dataset. In order to build a dialogue agent
with CS capability, we firstly inject external knowledge into a pre-trained
conversational model to establish basic commonsense through efficient Adapter
tuning (Section 4). Secondly, we propose the ``two-way learning'' method to
enable the bidirectional relationship between CS knowledge and sentence pairs
so that the model can generate a sentence given the CS triplets, also generate
the underlying CS knowledge given a sentence (Section 5). Finally, we leverage
this integrated CS capability to improve open-domain dialogue response
generation so that the dialogue agent is capable of understanding the CS
knowledge hidden in dialogue history on top of inferring related other
knowledge to further guide response generation (Section 6). The experiment
results demonstrate that CS\_Adapter fusion helps DialoGPT to be able to
generate series of CS knowledge. And the DialoGPT+CS\_Adapter response model
adapted from CommonGen training can generate underlying CS triplets that fits
better to dialogue context.Comment: this is a long paper, the short version was accepted by SemDial 202
Unified Conversational Models with System-Initiated Transitions between Chit-Chat and Task-Oriented Dialogues
Spoken dialogue systems (SDSs) have been separately developed under two
different categories, task-oriented and chit-chat. The former focuses on
achieving functional goals and the latter aims at creating engaging social
conversations without special goals. Creating a unified conversational model
that can engage in both chit-chat and task-oriented dialogue is a promising
research topic in recent years. However, the potential ``initiative'' that
occurs when there is a change between dialogue modes in one dialogue has rarely
been explored. In this work, we investigate two kinds of dialogue scenarios,
one starts from chit-chat implicitly involving task-related topics and finally
switching to task-oriented requests; the other starts from task-oriented
interaction and eventually changes to casual chat after all requested
information is provided. We contribute two efficient prompt models which can
proactively generate a transition sentence to trigger system-initiated
transitions in a unified dialogue model. One is a discrete prompt model trained
with two discrete tokens, the other one is a continuous prompt model using
continuous prompt embeddings automatically generated by a classifier. We
furthermore show that the continuous prompt model can also be used to guide the
proactive transitions between particular domains in a multi-domain
task-oriented setting.Comment: accepted by CUI 202
- …