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Register a new userWhy Should I Turn Left? Towards Active Explainability for Spoken Dialogue Systems.
لماذا يجب أن أتحول إلى اليسار؟نحو الشرط النشط لأنظمة الحوار المنطوقة.
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في هذه الورقة، نقول أن أنظمة الحوار قادرة على شرح قراراتها بنشاط يمكنها الاستفادة من المنطق المعني.نحن نحفز سبب هذه الاستراتيجية المناسبة ودمجها ضمن إطار مدير الحوار المؤخري الخاص بنا على أساس المنطق الخطي.على وجه الخصوص، يتيح ذلك نظام الحوار تقديم إجابات معقولة على السبب في الأسئلة التي تستعرضها المعلومات التي سبق إعطاءها بواسطة النظام.
In this paper we argue that to make dialogue systems able to actively explain their decisions they can make use of enthymematic reasoning. We motivate why this is an appropriate strategy and integrate it within our own proof-theoretic dialogue manager framework based on linear logic. In particular, this enables a dialogue system to provide reasonable answers to why-questions that query information previously given by the system.
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Spoken language understanding, usually including intent detection and slot filling, is a core component to build a spoken dialog system. Recent research shows promising results by jointly learning of those two tasks based on the fact that slot fillin
g and intent detection are sharing semantic knowledge. Furthermore, attention mechanism boosts joint learning to achieve state-of-the-art results. However, current joint learning models ignore the following important facts: 1. Long-term slot context is not traced effectively, which is crucial for future slot filling. 2. Slot tagging and intent detection could be mutually rewarding, but bi-directional interaction between slot filling and intent detection remains seldom explored. In this paper, we propose a novel approach to model long-term slot context and to fully utilize the semantic correlation between slots and intents. We adopt a key-value memory network to model slot context dynamically and to track more important slot tags decoded before, which are then fed into our decoder for slot tagging. Furthermore, gated memory information is utilized to perform intent detection, mutually improving both tasks through global optimization. Experiments on benchmark ATIS and Snips datasets show that our model achieves state-of-the-art performance and outperforms other methods, especially for the slot filling task.
The ability to take turns in a fluent way (i.e., without long response delays or frequent interruptions) is a fundamental aspect of any spoken dialog system. However, practical speech recognition services typically induce a long response delay, as it
takes time before the processing of the user's utterance is complete. There is a considerable amount of research indicating that humans achieve fast response times by projecting what the interlocutor will say and estimating upcoming turn completions. In this work, we implement this mechanism in an incremental spoken dialog system, by using a language model that generates possible futures to project upcoming completion points. In theory, this could make the system more responsive, while still having access to semantic information not yet processed by the speech recognizer. We conduct a small study which indicates that this is a viable approach for practical dialog systems, and that this is a promising direction for future research.
Most prior work on task-oriented dialogue systems is restricted to supporting domain APIs. However, users may have requests that are out of the scope of these APIs. This work focuses on identifying such user requests. Existing methods for this task m
ainly rely on fine-tuning pre-trained models on large annotated data. We propose a novel method, REDE, based on adaptive representation learning and density estimation. REDE can be applied to zero-shot cases, and quickly learns a high-performing detector with only a few shots by updating less than 3K parameters. We demonstrate REDE's competitive performance on DSTC9 data and our newly collected test set.
This paper aims at providing a comprehensive overview of recent developments in dialogue state tracking (DST) for task-oriented conversational systems. We introduce the task, the main datasets that have been exploited as well as their evaluation metr
ics, and we analyze several proposed approaches. We distinguish between static ontology DST models, which predict a fixed set of dialogue states, and dynamic ontology models, which can predict dialogue states even when the ontology changes. We also discuss the model's ability to track either single or multiple domains and to scale to new domains, both in terms of knowledge transfer and zero-shot learning. We cover a period from 2013 to 2020, showing a significant increase of multiple domain methods, most of them utilizing pre-trained language models.
In goal-oriented dialogue systems, users provide information through slot values to achieve specific goals. Practically, some combinations of slot values can be invalid according to external knowledge. For example, a combination of cheese pizza'' (a
menu item) and oreo cookies'' (a topping) from an input utterance Can I order a cheese pizza with oreo cookies on top?'' exemplifies such invalid combinations according to the menu of a restaurant business. Traditional dialogue systems allow execution of validation rules as a post-processing step after slots have been filled which can lead to error accumulation. In this paper, we formalize knowledge-driven slot constraints and present a new task of constraint violation detection accompanied with benchmarking data. Then, we propose methods to integrate the external knowledge into the system and model constraint violation detection as an end-to-end classification task and compare it to the traditional rule-based pipeline approach. Experiments on two domains of the MultiDoGO dataset reveal challenges of constraint violation detection and sets the stage for future work and improvements.
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