Building an Italian Written-Spoken Parallel Corpus: a Pilot Study
Authors: Elisa Dominutti, Lucia Pifferi, Felice Dell'Orletta, Simonetta Montemagni, Valeria Quochi (Università di Pisa, CNR-ILC, 2019)
Topic: Learner Corpora and Language Acquisition

Abstract
This paper presents a pilot study towards the creation of a monolingual written– spoken parallel corpus in Italian, featuring two main novelties in the general landscape of spoken corpora: the alignment with the written counterpart of the same content and the spoken variety dealt with, represented by transcriptions of radio news broadcasting.

Introduction
Nowadays, the contrast between written and spoken language does no longer represent a clear-cut opposition. The emergence of modern communication technologies such as radio, television and new (digital) media led to important changes in the analysis of the diamesic variation. Under this view, the opposition spoken vs. written language is reformulated in terms of a continuum with prototypical written and spoken language at the extreme poles and within which a cline of intermediate linguistic varieties can be recognised, mixing, to a different extent, features of the two. Nencioni (1976) defined the extreme poles of this continuum as the parlato-parlato (‘spoken-spoken’) variety, i.e. casual, spontaneous conversation, and the scritto-scritto (‘written-written’) variety, i.e. planned, formal, written language. Besides the typical contexts envisaging the use of spoken language—which require all participants to be present in the same environment, that the conversation is held in turns and that speakers make sure their messages are getting across—different contexts can be imagined: among them, the radio and television language which, despite being spoken, present traces of textual organisation recalling the written language. Nencioni (1976) qualifies this variety of language use as parlato-scritto (‘spoken-written’), a label that emphasises its hybrid nature characterised by the co-occurrence of traits typical of both written and spoken language. From a different perspective, Ong (1982) refers to this variety as ‘secondary orality’, i.e. “an orality not antecedent to writing and print, as primary orality is, but consequent and dependent upon writing and print”. In addition to this socio-linguistic interest, the issue also bears relevance for computational approaches as it has a substantial impact on the perceived naturalness of human-machine interaction. Indeed, one of the reasons why speech synthesis applications still produce unnatural speech, apart from bad prosody is that written language is generally not suitable, i.e. comprehensible, direct and effective, in spoken contexts (Kaji et al., 2004). With the rise and quick spread of Virtual Reality (VR) and Augmented-Reality (AR) applications, moreover, the mismatch between written and spoken language styles brings about serious technological limitations because unnaturalness of the virtual agents translates into bad human comprehension and/or distrust in those agents altogether. It is thus no longer sufficient to pass a written message to the speech synthesizer, but such a message needs to be transformed in a form suitable to be spoken in the specific context of use. In order to be able to do this, corpus data is needed such as a monolingual parallel aligned corpus of written and spoken texts about the same content. A corpus designed in this way is of fundamental importance for: a) investigating the features of the parlato-scritto language variety, its similarities and differences with respect to the written language; and b) for creating the prerequisites for the design and development of tools for monitoring the communicative effectiveness of texts with respect to their production mode and for supporting the semi-automatic generation or transformation of texts to be delivered orally. Such a corpus represents an important novel contribution in the area of language corpora; generally in fact corpora target either written or spoken language. Some corpora indeed also include sections with transcriptions of spoken language: see for instance the Brown corpus for English. On the front of spoken corpora, large corpora of spoken Italian were produced, some aiming at specific purposes, like CiT (Corpus di Italiano Trasmesso) (Spina, 2000) or LIR (Maraschio et al., 2004), while others aiming at representing Italian in a wider perspective like C-ORAL-ROM (Cresti and Moneglia, 2005). Some of them take into account only a few aspects of the linguistic variability, mainly the diaphasic and in some cases diamesic dimension. Our Corpus Italiano Parallelo Parlato Scritto (‘Spoken Written Italian Parallel Corpus’, henceforth CIPPS) features two fundamental novelties in the general landscape of spoken corpora: the alignment with a written counterpart of the same content and the type of spoken variety dealt with.

Conclusion
In this paper we have presented our first experiments towards the creation of the CIPPS, a monolingual written-spoken parallel aligned corpus. The data for this pilot was drawn from existing corpora and archives, it was automatically aligned on the basis of two statistical methods and finally manually validated. To the best of our knowledge, this is the first attempt to build such a corpus and more research is needed to improve its potentials and increase its magnitude. Among the open issues to be approached first is the lack of punctuation in the spoken part of the corpus, which makes automatic alignment with the written counterpart too coarse. As mentioned in the introduction, a corpus like ours might also be precious as a training set for the development of a system for transforming written into suitable spoken texts. Although little work has been done in this direction, the time is now ripe to tackle the challenge and we plan to start experimenting with both paraphrasing methods—as mentioned in section 1—and with monolingual machine translation, taking inspiration from Quirk et al. (2004) and Wubben et al. (2012). In this perspective, however, the first necessary step is to increase corpus size and improve alignment.