Alessandro Suglia, Yonatan Bisk, Ioannis Konstas, Antonio Vergari, Emanuele Bastianelli, Andrea Vanzo, Oliver Lemon. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. 2021.

Alessandro Suglia, Ioannis Konstas, Andrea Vanzo, Emanuele Bastianelli, Desmond Elliott, Stella Frank, Oliver Lemon. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020.

Alessandro Suglia, Antonio Vergari, Ioannis Konstas, Yonatan Bisk, Emanuele Bastianelli, Andrea Vanzo, Oliver Lemon. Proceedings of the 28th International Conference on Computational Linguistics. 2020.

Spoken Language Understanding infers semantic meaning directly from audio data, and thus promises to reduce error propagation and misunderstandings in end-user applications.However, publicly available SLU resources are limited.In this paper, we release SLURP, a new SLU package containing the following: (1) A new challenging dataset in English spanning 18 domains, which is substantially bigger and linguistically more diverse than existing datasets; (2) Competitive baselines based on state-of-the-art NLU and ASR systems; (3) A new transparent metric for entity labelling which enables a detailed error analysis for identifying potential areas of improvement.SLURP is available at

We present a new neural architecture for widecoverage Natural Language Understanding in Spoken Dialogue Systems.We develop a hierarchical multi-task architecture, which delivers a multi-layer representation of sentence meaning (i.e., Dialogue Acts and Frame-like structures).The architecture is a hierarchy of self-attention mechanisms and BiLSTM encoders followed by CRF tagging layers.We describe a variety of experiments, showing that our approach obtains promising results on a dataset annotated with Dialogue Acts and Frame Semantics.Moreover, we demonstrate its applicability to a different, publicly available NLU dataset annotated with domainspecific intents and corresponding semantic roles, providing overall performance higher than state-of-the-art tools such as RASA, Dialogflow, LUIS, and Watson.For example, we show an average 4.45% improvement in entity tagging F-score over Rasa, Dialogflow and LUIS.

The focus of this work is to exploit ontologies to make robotic systems more accessible to non-expert users, therefore supporting the deployment of robot-integrated applications. Due to the increasing number of robotic platforms available for commercial use, robotic systems are nowadays being approached by users with different backgrounds, who are often more interested in the robots' high-level capabilities than their technical architecture. Without the right expertise however, using robots is restricted to the capabilities exposed by the platform provider, i.e. they can only be used as end products rather than as development platforms. Our hypothesis is that an ontological representation of the capabilities of robots could make these capabilities more accessible, reducing the complexity of robot programming and enabling non-experts to exploit these systems to a much larger extent. To demonstrate this, an ontology abstracting the capabilities exposed by the most common robotic middleware (ROS) is integrated in a system to allow non-experts to program robots of different types and capabilities without previous knowledge either of the specific robotic platform being considered, or of the intricate systems used in its implementation. Our experiments, in which non-experts users had to configure the system in order to make robots achieve different tasks, show how the efforts required for realizing basic tasks using available robotic platforms can be sensibly reduced through our approach.

Modern robotic architectures are equipped with sensors enabling a deep analysis of the environment. In this work, we aim at demonstrating that such perceptual information (here modeled through semantic maps) can be effectively used to enhance the language understanding capabilities of the robot. A robust lexical mapping function based on the Distributional Semantics paradigm is here proposed as a basic model of grounding language towards the environment. We show that making such information available to the underlying language understanding algorithms improves the accuracy throughout the entire interpretation process.

Robots are slowly becoming part of everyday life, as they are being marketed for commercial applications (viz. telepresence, cleaning or entertainment). Thus, the ability to interact with non-expert users is becoming a key requirement. Even if user utterances can be efficiently recognized and transcribed by Automatic Speech Recognition systems, several issues arise in translating them into suitable robotic actions. In this paper, we will discuss both approaches providing two existing Natural Language Understanding workflows for Human Robot Interaction. First, we discuss a grammar based approach: it is based on grammars thus recognizing a restricted set of commands. Then, a data driven approach, based on a free-from speech recognizer and a statistical semantic parser, is discussed. The main advantages of both approaches are discussed, also from an engineering perspective, i.e. considering the effort of realizing HRI systems, as well as their reusability and robustness. An empirical evaluation of the proposed approaches is carried out on several datasets, in order to understand performances and identify possible improvements towards the design of NLP components in HRI.