Distributed Processes for Spoken Questions and Commands Understanding
Authors: Dario Di Mauro, Antonio Origlia, Francesco Cutugno (Università di Napoli Federico II, Università di Padova, 2017)
Topic: Chatbots and Dialogue Systems

Abstract
Commercial products labelled as smart devices usually recur to a centralised system that processes all the requests. A distributed model, where nodes independently interact with the environment, may provide a widespread support for both users and other devices. In the latter setup, each entity has a partial awareness about defines the requests accepted by the network, and this aspect complicates the task. This paper improves an existing distributed model, called PHASER, by proposing linguistic analysis techniques to manage non-matching requests. NLP methods produce a confidence; any PHASER node forwards non-matching requests to close peers. PHASER exploits the confidence to rank the adjacent peers and deliver the question to the best node. Partial Matching and a Bag-of-Words models will be compared with the currently adopted full matching. The Bag-of-Words approach offered the best results in terms of both quality and required time.

Introduction
This paper introduces a new distributed approach to question answering and command execution in different Intelligent Environments (henceforth IE). This idea is rarely encountered in literature (with a few exceptions by AUTHOR). IE is a new discipline including Domotic, Internet of Things, Cultural Heritage Technological Innovation and other similar issues. In our approach, devices in the environments constitute nodes of a network and this network provides services to an interacting user. Reasons of interests for NLP studies in this kind of application lie in the idea that user requests are delivered using Natural Language (mainly speech) in the simplest case, or multimodally by integrating speech with gestures and interaction with physical controls. Nowadays, smart devices commonly propose interaction through natural language to the users. As the services offer becomes wider, a network of specialised applications managing very specific domains is masked behind a single named character (Alexa, Siri, Cortana). While this is common for single devices hosting multiple applications, which inform the operating system about their capabilities through dedicated languages (e.g. SRGS, AUTHOR), the model is being transferred to networks of different devices. Optimising the communication between these devices is a critical issue to reduce response times, balance communication and improve quality of service. In this paper, we will concentrate on showing how the confidence metric, commonly available in NLP techniques, can be exploited to support rapid adaptation of the network to request dispatching, avoiding broadcasting. We will mainly describe the simplest case of speech interaction and understanding; for a view on the complex multimodal approach, please refer to AUTHOR. Given these premises, nodes in the given network are able to respond to simple questions or commands uttered by the user. In a first approach, utterances should conserve a coherence with the ``nature'' of the node, i.e. if I am ``talking'' to the kitchen or to the microwave oven, I should make requests strictly inherent with the device functions. On the contrary, we wish to expand the ``intelligence'' of the environment giving to the user the possibility to make any kind of requirements to any node in the network. In this view, each node is able to classify the string deriving by the speech utterance assigning it to one of the many classes of relevant action the environment can realise, even if the node itself is not able to execute that action. The introduction of a distributed knowledge base and of network information spreading techniques concur to the realisation of an environment extremely reactive, scalable and easily configurable for different domains. The system is reactive as the network connections are strongly optimised: redundant and rarely used paths are pruned. Mechanisms for knowledge distribution are optimised in order to deliver the proper answer minimising network reaction times. The system is easily configurable to different domains as this kind of networks just require a formal description of the semantics of each node, of the action classes they are able to process, and of the most probable connection among nodes that a-priori the environment designer implements. In order to realise this system, many NLP software modules are needed, and among these: an automatic spoken dialogue manager, a Spoken Language Understanding system, an ontology modelling the environment and the devices. An extended description of each part can be found in AUTHOR; this paper focuses on a linguistic analysis to improve the navigation of the request through the network. In Section sec:related we present related works; Section sec:model recalls the model of our system. In Section sec:navigation we discuss a network of interactive entities, highlighting differences about the current version and the contribution of this paper. Experiments and Discussion are presented in Sections sec:experiment and sec:discussion. Section sec:conclusion concludes the paper.

Conclusion
This paper presented PHASER, a distributed model for Human-Computer Interaction in Intelligent Environments. This work aims at improving the Navigation Problem, where a node forwards a received command if it is not able to understand or process it. Since the node operates with partial knowledge about both the request and the environment, it tries to analyse the input and choose the best adjacent node. The most crucial part is not a refined linguistic analysis of each request, but a quick confidence on how much each adjacent node could be a good candidate to understand that request. This requirement is motivated by two reasons: (i) this process is part of a longer step where a user is waiting for a response; (ii) all the evaluations rely on information each node shares with others. In order to deeply understand the command, the node should expose sensible data and it is not always desired in a distributed context. The work focused on three strategies: perfect and partial matches with regular expressions and a bag-of-words model. This last approach has given the best results with positive aspects mainly related to easy network design and security of each node. The investigated methods are just used to rank close peers on as an out-of-context request reaches the current node. It operates without understanding the request, so finer considerations are not possible. The considered approaches do not limit PHASER nodes in adopting more refined techniques in assessing and categorising an input request.