ItGraSyll: A Computational Analysis of Graphical Syllabification and Stress Assignment in Italian
Authors: Liviu P. Dinu, Bogdan Iordache, Bianca Guita, Simona Georgescu, Alina Cristea (University of Bucharest, Human Language Technologies Research Center, 2024)
Topic: Multimodal

Abstract
In this paper we build a dataset of Italian graphical syllables (called ItGraSyll). We perform quantitative and qualitative analyses on the syllabification and stress assignment in Italian. We propose a machine learning model, based on deep-learning techniques, for automatically inferring syllabification and stress assignment. For stress prediction we report 94.45% word-level accuracy, and for syllabification we report 98.41% word-level accuracy and 99.82% hyphen-level accuracy.

Introduction
Word syllabification and syllable analysis are two related issues of great importance in the study of language (written or spoken). These topics have attracted a large category of researchers, from pure linguists, in phonetics, to psycholinguists, computer scientists, speech therapists, etc. Thus, the syllable plays an important role in language learning and acquisition, speech recognition, speech production AUTHOR, language similarity AUTHOR, in text comprehensibility (Kincaid-Flesch formula AUTHOR), in speech therapy, in poetry analysis AUTHOR, etc. Each language has its own way of grouping sounds into syllables and its own rules for dividing words into syllables. Linguistically, the syllable represents "the smallest phonetic trance likely to receive an accent and only one" AUTHOR, and the syllabic cut is seen by \citet{de2011course} on the border between the implosion and the explosion of the spoken sound: "If in a chain of sounds one goes from implosion to explosion, one obtains a particular effect which is the indication of the boundary of the syllable". The analysis of the words’ syllabic structure also plays an important part in historical linguistics AUTHOR, not only in diachronic phonetics and phonology, but also in lexicology. Romance comparative linguistics, in particular, still needs a detailed overview of this aspect, as syllable, segmentation and prosody can give strong account on phonetic changes that haven’t been explained yet. The “prosodic revolution” AUTHOR from Latin to the Romance languages – including syncope (the loss of an intermediate syllable) and apocope (the loss of the final syllable) at a large scale – has led to major changes, but their weight is different from one idiom to another: while the Western Romance languages manifest highly evident differences from the Latin phonological and prosodic system, and the Eastern languages are considered to be most conservative from this point of view, Italian seems to be in between AUTHOR. On the other hand, in Latin, the relation between stress and quantity grew stronger, thus short stressed vowels progressively gained length. It is noteworthy that this situation is best preserved in Italian, and not in the Eastern Romance idioms: thus, in Italian stress cannot skip a heavy penultimate syllable, and stress cannot fall further back than the antepenultimate syllable, a twofold characteristic feature of the Latin prosodic system. This is why we are taking Italian as a starting point for a larger-scale study, oriented towards all Romance languages. The main difference between Latin and its modern descendants is that Latin stress was quantity- sensitive, leading thus to the following rule: in polysyllabic words, stress fell on a heavy penultimate (meaning, containing a long vowel), otherwise on the antepenultimate. Due to the collapse of vowel quantity as a distinctive feature in the vocalic system, no Romance language has retained the Latin stress rule as such AUTHOR. As, from a statistic point of view, the greatest part of the Romance lexicon is represented by penultimate stressed words, a basic automatic mechanism would assign penultimate stress by default, whereas for both final and antepenultimate stress, the machine (as well as, not in a few cases, non-native speakers) would need further specification. As a consequence of the loss of Latin vowel quantity, Romance stress has ceased to be completely predictable. That is, partially, why in the majority of the traditional Romance comparative or historical grammars, there is no specific section devoted to syllabification AUTHOR, or, if there is, it focuses either on general prosodic features AUTHOR, or on the vowel evolution depending on its presence in an open or closed syllable AUTHOR. The lack of a section dedicated to syllabification is also common in the historical grammars of Italian AUTHOR. We will focus in this research only on written form of words, so we will investigate only the graphical syllabification and stress. By focusing on the graphical syllabification and stress in Italian, we aim to take a step forward towards the complete evaluation of the prosodic changes that took place in the transition from Latin to the Romance languages, and their influence on the Romance phonetics and phonology. A machine- learning model, capable of automatically inferring graphical syllabification and stress assignment, along with the purpose of creating a data-base containing the quantitative and qualitative description of syllabification and stress in the Romance languages, could be the first important task in the greater challenge of tracing the similarities and differences between the Romance languages and, more important, between Romance and Latin. From a typological point of view, the study of syllabification and stress can shed a new light on the universal features that, by defining our phonoarticulatory and phonoacoustic apparatus, have guided the languages’ development and change. Given the promising results of this analysis, the present study can establish the basis of a research of the syllable in other languages, either linguistically or typologically related to Italian. One of the studies that address automatic syllabification in Italian belongs to \citet{bigi2014generic}, who proposed a tool that performs rule-based automatic segmentation. \citet{adsett_et_all} and \citet{DBLP:journals/csl/AdsettMK09} investigated whether data-driven approaches outperform rule-based approaches for a language with a low syllabic complexity, such as Italian. The authors reached the conclusion that even in this case data-driven systems are the more appropriate approach. In terms of machine learning, the tasks of automatically inferring syllable boundaries and predicting stress assignment can be naturally framed as sequence labeling problems. While automatic syllabification has received more attention recently AUTHOR, stress placement has not been investigated as much AUTHOR. Given the complexity of syllable applications and word syllabification, the presence of electronic resources dedicated to them becomes a necessity. While native speakers of a language generally do not have great difficulty in spelling words, the same cannot be said of those who learn a foreign language who often tend to apply their own rules to foreign words, and problems arise in automatic syllabification. This is because the rules of syllabification are linguistic rules, and they cannot always be easily modeled by the computer when there are no other linguistic factors that those rules take into account. For example, a rule that is present in many languages distinguishes between a vowel and a semivowel, but the computer is not able to easily recognize when the same sign has the value of a vowel and when it is a semivowel. Because of this, rule-based adaptations of syllabification systems AUTHOR generally have higher errors, and many languages do not have an automatic syllabification system yet (for example, in the Python library, only a few languages have syllabification). The last few decades have brought the first data-driven syllabification systems. However, in order to build such a system, training data is needed, and there are many cases in which the available data do not cover the whole language, and thus the systems have different results when the test corpus is changed. Starting with these remarks, our main contributions are: \item We propose ItGraSyll (Italian graphical syllables), a dataset of 114,503 Italian words, in orthographic form, containing annotations for their orthographic syllabification and stress placement \item We perform quantitative and qualitative analyses of the previously built dataset. \item We analyze stress placement in the context of the Italian syllables. \item We propose an automatic system of syllabification for Italian words.

Conclusion
In this paper we have investigated graphical syllabification and graphical stress assignment for Italian words. We have started by building ItGraSyll, a dataset of Italian graphical syllabified words, with stress annotations as well, on which we have performed several quantitative and qualitative analyses, including the verification of two minimum effort laws for the case of Italian. Finally, we have proposed a recurrent neural network machine learning model for automatic syllabification and stress assignment for Italian written words. For stress prediction we have obtained 94.45% word-level accuracy, and for syllabification we have obtained 98.41% word-level accuracy and 99.82% hyphen-level accuracy. In future work we intend to extend the analysis from dictionary level to corpus level and to investigate other languages as well. We want to thank the reviewers for their useful suggestions. Research supported by the Ministry of Research, Innovation and Digitization, CNCS/CCCDI UEFISCDI, SiRoLa project, number PN-IV-P1-PCE-2023-1701, Romania.