Multifunctional ISO Standard Dialogue Act Tagging in Italian
Authors: Gabriel Roccabruna, Alessandra Cervone, Giuseppe Riccardi (Università di Trento, Amazon Alexa AI, 2020)
Topic: Chatbots and Dialogue Systems

Abstract
The task of Dialogue Act (DA) tagging, a crucial component in many conversational agents, is often addressed assuming a single DA per speaker turn in the conversation. However, speakers' turns are often multifunctional, that is they can contain more than one DA (i.e. ``I'm Alex. Have we met before?'' contains a `statement', followed by a `question'). This work focuses on multifunctional DA tagging in Italian. First, we present iLISTEN2ISO, a novel resource with multifunctional DA annotation in Italian, created by annotating the iLISTEN corpus with the ISO standard. We provide an analysis of the corpus showing the importance of multifunctionality for DA tagging. Additionally, we train DA taggers for Italian on iLISTEN (achieving State of the Art results) and iLISTEN2ISO. Our findings indicate the importance of using a multifunctional approach for DA tagging.

Introduction
Dialogue Acts (DAs), a linguistically motivated model of speakers' intentions in a conversation, play a crucial role for several conversational AI tasks. DAs have been successfully used as part of conversational agents components, for example for Spoken Language Understanding AUTHOR or Natural Language Generation, and for response generation AUTHOR. Moreover, DAs have been shown to be important features to learn the intentional structure of conversations AUTHOR. One of the bottlenecks for current research on DAs is the lack of publicly available resources with DA annotation. While this is true also for English, it is even more important for languages with fewer resources, such as Italian. For Italian, the only publicly available resource with DA annotation is currently the iLISTEN corpus AUTHOR, released for EVALITA in 2018. While useful, this resource relies on an annotation scheme which assumes only one single DA per conversational turn (see Figure). However, ISO 24617-2 AUTHOR, the latest accepted standard for DA annotation, posits that conversational turns can be multifunctional in a sequential way, i.e. speakers' turns can be composed of multiple DAs in sequence AUTHOR. In this work, we investigate the task of multifunctional DA tagging in Italian. The contributions of this paper are: (1) we create iLISTEN2ISO, to the best of our knowledge the first publicly available resource with DA annotation in Italian which uses a multifunctional approach and is ISO-standard compliant; (2) we present an analysis of iLISTEN2ISO showing the importance of multifunctional DA annotation; (3) we propose baseline DA tagging models for Italian trained on iLISTEN (achieving, to the best of our knowledge, SOTA results) and iLISTEN2ISO.

Conclusion
We presented iLISTEN2ISO, a resource for Italian multifunctional DA tagging using ISO 24617-2. We argued the importance to consider turns as a composition of multiple communicative functions, in order to preserve important semantic information. Moreover, we presented different baseline DA tagging models, on both iLISTEN and iLISTEN2ISO. We believe the presented resource could be useful to the research community for experimenting with multifunctional DA tagging in Italian, as well as cross-corpora DA tagging. As future work, we plan to explore joint DA segmentation and classification in Italian, for example taking inspiration from the work presented by Zhao and Kawahara (2019).