ItGraSyll: A Computational Analysis of Graphical Syllabification and Stress Assignment in Italian Liviu P. Dinu1,3,* , Bogdan Iordache1,3 , Bianca Guita3 , Simona Georgescu2,3 and Alina Cristea3 1 University of Bucharest, Faculty of Mathematics and Computer Science, Romania 2 University of Bucharest, Faculty of Foreign Languages and Literatures, Romania 3 Human Language Technologies Research Center, Bucharest, Romania 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. Keywords syllabification, stress assignment, Italian, 1. Introduction “prosodic revolution” [10] from Latin to the Romance lan- guages – including syncope (the loss of an intermediate Word syllabification and syllable analysis are two related syllable) and apocope (the loss of the final syllable) at a issues of great importance in the study of language (writ- large scale – has led to major changes, but their weight is ten or spoken). These topics have attracted a large cat- different from one idiom to another: while the Western egory of researchers, from pure linguists, in phonetics, Romance languages manifest highly evident differences to psycholinguists, computer scientists, speech thera- from the Latin phonological and prosodic system, and the pists, etc. Thus, the syllable plays an important role in Eastern languages are considered to be most conservative language learning and acquisition, speech recognition, from this point of view, Italian seems to be in between speech production [1, 2], language similarity [3], in text [10]. On the other hand, in Latin, the relation between comprehensibility (Kincaid-Flesch formula [4]), in speech stress and quantity grew stronger, thus short stressed therapy, in poetry analysis [5, 6], etc. Each language has vowels progressively gained length. It is noteworthy that its own way of grouping sounds into syllables and its own this situation is best preserved in Italian, and not in the rules for dividing words into syllables. Linguistically, the Eastern Romance idioms: thus, in Italian stress cannot syllable represents "the smallest phonetic trance likely skip a heavy penultimate syllable, and stress cannot fall to receive an accent and only one" [7], and the syllabic further back than the antepenultimate syllable, a twofold cut is seen by De Saussure [8] on the border between the characteristic feature of the Latin prosodic system. This implosion and the explosion of the spoken sound: "If in is why we are taking Italian as a starting point for a larger- a chain of sounds one goes from implosion to explosion, scale study, oriented towards all Romance languages. The one obtains a particular effect which is the indication of main difference between Latin and its modern descen- the boundary of the syllable". dants is that Latin stress was quantity- sensitive, leading The analysis of the words’ syllabic structure also plays thus to the following rule: in polysyllabic words, stress an important part in historical linguistics [9], not only fell on a heavy penultimate (meaning, containing a long in diachronic phonetics and phonology, but also in lexi- vowel), otherwise on the antepenultimate. Due to the cology. Romance comparative linguistics, in particular, collapse of vowel quantity as a distinctive feature in the still needs a detailed overview of this aspect, as syllable, vocalic system, no Romance language has retained the segmentation and prosody can give strong account on Latin stress rule as such [10]. As, from a statistic point of phonetic changes that haven’t been explained yet. The view, the greatest part of the Romance lexicon is repre- sented by penultimate stressed words, a basic automatic CLiC-it 2024: Tenth Italian Conference on Computational Linguistics, mechanism would assign penultimate stress by default, Dec 04 — 06, 2024, Pisa, Italy * Corresponding author. whereas for both final and antepenultimate stress, the $ ldinu@fmi.unibuc.ro (L. P. Dinu); machine (as well as, not in a few cases, non-native speak- iordache.bogdan1998@gmail.com (B. Iordache); ers) would need further specification. As a consequence bianca.guita@s.unibuc.ro (B. Guita); of the loss of Latin vowel quantity, Romance stress has simona.georgescu@lls.unibuc.ro (S. Georgescu); ceased to be completely predictable. That is, partially, alinaciobanu20@gmail.com (A. Cristea) © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License why in the majority of the traditional Romance compara- Attribution 4.0 International (CC BY 4.0). CEUR ceur-ws.org Workshop ISSN 1613-0073 Proceedings tive or historical grammars, there is no specific section other linguistic factors that those rules take into account. devoted to syllabification [11], or, if there is, it focuses For example, a rule that is present in many languages either on general prosodic features [12], or on the vowel distinguishes between a vowel and a semivowel, but the evolution depending on its presence in an open or closed computer is not able to easily recognize when the same syllable [13]. The lack of a section dedicated to syllab- sign has the value of a vowel and when it is a semivowel. ification is also common in the historical grammars of Because of this, rule-based adaptations of syllabification Italian [14, 11, 15]. We will focus in this research only systems [26] generally have higher errors, and many lan- on written form of words, so we will investigate only guages do not have an automatic syllabification system the graphical syllabification and stress. By focusing on yet (for example, in the Python library, only a few lan- the graphical syllabification and stress in Italian, we aim guages have syllabification). The last few decades have to take a step forward towards the complete evaluation brought the first data-driven syllabification systems. of the prosodic changes that took place in the transition However, in order to build such a system, training from Latin to the Romance languages, and their influence data is needed, and there are many cases in which the on the Romance phonetics and phonology. A machine- available data do not cover the whole language, and thus learning model, capable of automatically inferring graph- the systems have different results when the test corpus ical syllabification and stress assignment, along with the is changed. purpose of creating a data-base containing the quanti- Starting with these remarks, our main contributions tative and qualitative description of syllabification and are: stress in the Romance languages, could be the first im- portant task in the greater challenge of tracing the simi- • We propose ItGraSyll (Italian graphical syllables), larities and differences between the Romance languages a dataset of 114, 503 Italian words, in ortho- and, more important, between Romance and Latin. From graphic form, containing annotations for their or- a typological point of view, the study of syllabification thographic syllabification and stress placement1 and stress can shed a new light on the universal features • We perform quantitative and qualitative analyses that, by defining our phonoarticulatory and phonoacous- of the previously built dataset. tic apparatus, have guided the languages’ development • We analyze stress placement in the context of the and change. Given the promising results of this analysis, Italian syllables. the present study can establish the basis of a research of • We propose an automatic system of syllabification the syllable in other languages, either linguistically or for Italian words. typologically related to Italian. One of the studies that address automatic syllabifi- cation in Italian belongs to Bigi and Petrone [16], who 2. Quantitative Analysis proposed a tool that performs rule-based automatic seg- mentation. Adsett and Marchand [17] and Adsett et al. In this section we perform various measurements regard- [18] investigated whether data-driven approaches out- ing the syllables and stress placement of Italian written perform rule-based approaches for a language with a words and analyze the results. We perform, on Italian, low syllabic complexity, such as Italian. The authors an investigation similar to a previous investigations con- reached the conclusion that even in this case data-driven ducted on Romanian by Dinu and Dinu [27], Dinu and systems are the more appropriate approach. In terms of Dinu [28]. machine learning, the tasks of automatically inferring syl- lable boundaries and predicting stress assignment can be 2.1. Data naturally framed as sequence labeling problems. While We build a dataset of Italian words starting from the automatic syllabification has received more attention re- online version of Dizionario italiano De Mauro,2 which cently [19, 20, 21, 22, 23, 24], stress placement has not provides information regarding graphical syllabification been investigated as much [25]. and stress placement for the Italian vocabulary. Stressed Given the complexity of syllable applications and word syllables are also shown by having accents on the domi- syllabification, the presence of electronic resources dedi- nant vowel. Going further, this dataset will be referred cated to them becomes a necessity. While native speakers to as ItGraSyll. of a language generally do not have great difficulty in We performed several pre-processing steps. We spelling words, the same cannot be said of those who cleaned the resulted dataset by removing duplicates, pre- learn a foreign language who often tend to apply their fixes and suffixes in order to remain with the base word; own rules to foreign words, and problems arise in au- tomatic syllabification. This is because the rules of syl- 1 The dataset is available for research purposes upon request at: labification are linguistic rules, and they cannot always https://nlp.unibuc.ro/resources.html#itgrasyll be easily modeled by the computer when there are no 2 https://dizionario.internazionale.it/ abbreviations and unwanted punctuation marks such Index Syllable Frequency as dots, commas, apostrophes and dashes were also ex- 1 to 23943 cluded so we can correctly process each word and its 2 re 18199 syllable division. Finally, the dataset consists of 114, 503 3 ta 12796 words in orthographic form having between one and 4 te 10987 eleven syllables. The distribution of words per number 5 si 10026 of syllables is represented in Table 1. 6 a 9142 7 co 8874 #syll. #words Examples 8 ri 8868 9 ca 8478 1 722 ai 10 ra 8388 2 5,960 àc-cia 11 na 8367 3 23,286 àb-ba-co 12 ti 8184 4 41,253 a-ba-chì-sta 13 ne 8112 5 28,357 a-bi-tà-co-lo 6 10,829 ac-cu-mu-la-zió-ne 14 men 7841 7 3,294 au-ten-ti-fi-ca-zió-ne 15 la 7175 8 650 a-e-ro-mo-del-lì-sti-co 16 di 6663 9 132 bi-o-me-te-o-ro-lo-gì-a 17 le 6555 10 16 in-tel-let-tu-a-li-sti-ca-mén-te 18 li 6176 11 5 ge-ne-ra-ti-vo-tra-sfor-ma-zio-nà-le 19 no 5748 20 lo 5479 Table 1 Number of words per number of syllables. Table 2 Top 20 most frequent syllables. 2.2. Syllables the most frequent consonant-vowel structures are the We identified #𝑇 𝑦𝑝𝑒𝑠𝑦𝑙 = 3730 (type syllables) in following: a) for the type syllables: cvc (25%), ccvc (20.9%), Italian. The total number of syllables (token syllables) cvvc (7.79%). b) for the token syllables: cv (58%), cvc (15%), is #𝑇 𝑜𝑘𝑒𝑛𝑠𝑦𝑙 = 483, 931. So, the average length ccv (7%), cvv (4.74%) and v (4.32%). Moreover, we observe of a word measured in syllables is 𝑊 𝑜𝑟𝑑𝑠𝑎𝑣−𝑠𝑦𝑙 = that the cv structure corresponds to 40 out of the most 483,931/114,503 = 4.226. The 114,503 words are formed of frequent 50 syllables from the dataset. #𝐿𝑒𝑡𝑡𝑒𝑟𝑠 = 1,133,515 letters (graphemes). So, the aver- age length of a word measured in letters is 𝑊 𝑜𝑟𝑑𝑎𝑣−𝑙𝑒𝑡 2.4. Stress Placement = 1,133,515/114,503 = 9.899. In order to characterize the average length of a syllable We identified a total of 2,883 stressed syllables (type syl- measured in letters, we investigated two cases: a) the lables). So, 847 syllables are never stressed. The most average length of the token syllables measured in letters frequent 20 stressed syllables are represented in Table 3. is: 𝐿𝑆𝑦𝑙𝑡𝑜𝑘𝑒𝑛 = 1,133,515/483,931 = 2.342 b) the type We observe that the most frequent stressed syllable (men) syllables are formed of #𝑇 𝑦𝑝𝑒𝑆𝑦𝑙𝑙𝑒𝑡 = 13,576 letters. has a very high stress ratio (90%) when we compare the Thus, the average length of a type syllable measured in stressed occurrences with all its occurrences (stressed letters is 𝐿𝑆𝑦𝑙𝑡𝑦𝑝𝑒 = 13,576/3,730 = 3.639. and unstressed) in our database. While in the top 20 of These statistics are computed for the words extracted all syllables, men is the only syllable of length 3 (on the from the dictionary, which were considered to be equally 14th position), for stressed syllables there are a couple weighted. This excludes any information relating to the of other syllables with a length greater than 2 (zio on frequency of the words with respect to writing or speech. position 6 with 34% stress ratio, gia on position 19 with For future research, large corpora of Italian texts can be 65% stress ratio). leveraged in order to recompute these values and include We investigate stress placement with regard to syllable frequency-based weights. structure and we provide in Table 4 the percentages of A list of the most frequent 20 syllables is included in words having the stress placed on different positions (for Table 2. top 5), counting syllables from the beginning and from the end of the words as well. We observe that in most 2.3. Syllable Structure cases the stress is placed on the second to last syllable. We identified a total of 67 different consonant-vowel structures. The most frequent 7 structures cover almost 97% of the total. Depending on the type-token ratio, Index Syllable Frequency Stress ratio (%) 100 cover 74% and the most frequent 150 syllables (i.e. 1 men 7120 90 4% of #𝑇 𝑦𝑝𝑒𝑠𝑦𝑙 ) cover 80% of #𝑇 𝑜𝑘𝑒𝑛𝑠𝑦𝑙 . Over this 2 ta 5809 45 number, the percentage of coverage rises slowly. 2,281 3 na 3348 40 (61%) syllables of type syllables occur less then 10 times, 4 to 3254 15 and 1,174 syllables occur only once (hapax legomena). 5 la 2978 41 6 zio 2916 76 2.5.2. Stressed Syllables 7 ti 2820 34 8 ca 2461 29 A similar trend can be observed also for the stressed syl- 9 ra 2297 27 lables. Further, we notice that the most frequent syllables 10 li 2239 36 cover a wide ratio of the total syllable frequency. For 11 ri 2100 24 example, the 10 most frequent stressed syllable represent 12 tu 2024 62 31% of the total of stressed syllables, the top 50 syllables, 13 za 2022 42 60% and the top 200 syllables, 81% of the token syllables. 14 ni 1734 40 15 tri 1458 60 The values are plotted in Figure 1, for all syllables and 16 ma 1209 25 for stressed syllables. 17 si 1144 11 18 da 1109 43 Type 0.8 all syllables 19 gia 1081 65 stressed syllables 20 mi 1052 25 0.7 Table 3 0.6 Coverage Top 20 most frequent stressed syllables. The stress ratio indi- 0.5 cates how often out of all the occurrences of the syllable in 0.4 the corpus it appears as stressed. 0.3 25 50 75 100 125 150 175 200 Syllable %words Syllable %words Number of syllables 1st 8,611 1st 3,330 2nd 25,544 2nd 94,225 Figure 1: The coverage of most frequent syllables. 3rd 40,568 3rd 16,113 4th 25,593 4th 14 5th 9,243 5th 1 This results proves that the law is true for Italian too, (a) counting syllables from (b) counting syllables from a very small number of syllables cover a large part from the beginning of the the end of the word Italian language (there are necessary only 150 syllables word to cover 80% from language). Table 4 3. Minimum Effort Laws Stress placement for Italian. In this section we discuss two minimum effort laws that have been previously investigated for other languages 2.5. Syllables’ Usage and verify whether they apply for Italian as well. The syllables have a less intuitive behaviour, usually a small number of syllables cover a large part from a lan- 3.1. Chebanow guage. This is valuable for a large category of natural Denoting by 𝐹 (𝑛) the∑︀frequency∑︀ of a word having n languages, including English, Dutch, Romanian [28], Ko- syllables and by 𝑖 = 𝑛𝐹 (𝑛)/ 𝐹 (𝑛) the average rean, Chinese, etc. We investigate here if this empirical length (measured in syllables) of the words, Chebanow law is also applicable to Italian. We made this investiga- [29] proposed the following law between the average 𝑖 tion both on stressed and general syllables. and the probability of occurrences 𝑃 (𝑛) of the words having n syllables: 2.5.1. General Syllables (𝑖 − 1)𝑛−1 The most frequent 30 Italian syllables (when stress place- 𝑃 (𝑛) = * 𝑒1−𝑖 (1) (𝑛 − 1)! ment is disregarded) cover almost 50% of #𝑇 𝑜𝑘𝑒𝑛𝑠𝑦𝑙 , the most frequent 50 syllables cover 61%, the most frequent For Italian, 𝑖 = 4.226. (a) The probability distribution of the (b) Theoretical representation of the prob- (c) Menzerath’s Law: The more syllables in length of words. ability distribution of the length of a word, the smaller its syllables. words. Figure 2: Minimum effort laws. Model Hyphen Acc. Hyphen F1 Word Acc. GRU for syllabification w/o stress markers 99.74% 99.69% 97.61% GRU for syllabification w/ stress markers 99.82% 99.79% 98.41% GRU for stress prediction — — 94.45% Table 5 Performance metrics computed for the automatic syllabification and stress prediction on the test set. We computed accuracy and F1 scores on the sequence labelling predictions for syllabification, in order to assess how well the model predicts the positions where the syllables split. Word level metrics were computed for both syllabification and stress prediction; this kind of metrics are more strict since any misplaced hyphen in the syllabification makes the entire prediction wrong. In Figures 2a and 2b we plot the probability distribution 4. Automatic Syllabification and of the length of words (in syllables) – the practical and theoretical representations. Stress Assignment We observe that the two curves have comparable We further investigate how a deep-learning model can au- shapes, with a more prominent peak for the probabil- tomatically infer the syllabification and stress assignment ity distribution in Figure 2a; this peak can be influenced of Italian words, given their orthographic representation. by the fact that it is determined based on all the words in the dictionary, where many 4-syllable words are present. 4.1. Methodology 3.2. Menzerath Both tasks can be defined in terms of a sequence la- belling problem, strategy which was previously success- Menzerath’s law – later generalized by the Menzerath- ful used for Romanian[31, 32]. Let us consider, for ex- Altmann law [30] – states that the bigger the number of ample, the word medaglione (the Italian translation of syllables in a word, the lesser the number of phonemes the word "locket"). For syllabification we can label each composing these syllables. In other words, Menzerath’s letter from the word either with the label 1, denoting law expresses a negative correlation between the length that a syllable starts from that letter, or with the label of a word in syllables and the lengths in phonemes of its 0, meaning the respective letter is not the first letter in constitutive syllables. In cognitive economy terms, this its syllable. Similarly, for identifying the stressed vowel, means that the more complex a linguistic construct, the we can label its position with a 1 and all other letters smaller its constituents. The law is expressed as follows: are assigned the label 0. We thus obtain for our exam- ple the sequence 1010100010 for syllabification and the 𝑦 = 𝛼𝑥𝛽 𝑒−𝛾𝑥 (2) sequence 0000000100 for stress prediction (i.e. me-da- where 𝑦 is the syllable length (the size of the constituent), gliò-ne, the o vowel is stressed). 𝑥 is the number of syllables per word (the size of the lin- With these definitions, we can now construct machine guistic construct), and 𝛼, 𝛽, 𝛾 are empirical parameters. learning models for labelling the character sequences. Figure 2c shows that the law is satisfied for Italian. The model we propose is a recurrent neural network based on Gated Recurrent Units (GRU) [33]. The model ar- chitecture is comprised from the following components: • a character embedding layer, producing 64- 4.2. Results Anaysis dimensional vectors for each unique character Table 5 contains the metrics computed on the test set, • a stacked bidirectional GRU, with 3 layers and a using the models trained for syllabification (both with 128-dimensional hidden state; a 0.2-rate dropout and without stress markers) and the model trained for applied after each of the first two layers predicting the stressed vowel. We obtained a remarkable • 0.5-rate dropout, after the last GRU layer, along hyphen accuracy of 99.74% for syllabification without with one-dimensional batch normalization the stress markers, and, when we add the stress markers, • a time-distributed fully-connected layer with 256 we obtained an increasing accuracy, obtaining 99.82%. output nodes and ReLU activation Including the stress markers into the data used for syl- • a linear layer that projects the 256-dimensional labification improved the metrics across the board, most vector into a single number, on which sigmoid notably with a ∼ 1% increase in word-level accuracy, activation is applied to infer the binary labels. which considering the large amount of data, and the high accuracy scores is a significant improvement (460 fewer For training the models for both tasks, the dataset of syllabification mistakes as opposed to the approach that words is split into 50% training examples and 50% test excludes stress markers). Regarding the stress prediction, examples, unseen during training. we obtained an accuracy of 94.45%. Table 6 showcases a The loss function computed for the prediction made series of wrong predictions generated by the models on for a word, regardless of the task on which the model the tests sets for stress assignment and syllabification. is trained, is the average of two terms: the first one is We also look into the accuracy scores computed for the average character-wise binary cross-entropy, while the test set, when it is bucketed based on the real number the second one is the root mean squared error computed of syllables of the test words. These results are shown between the vector of predicted labels and the ground- in Figure 3 and Table 7. For stress assignment, accu- truth vector. The model is optimized using the Adam racy decreases to a global minimum for disyllabic words, optimizer [34], with a learning rate of 0.0003, no weight then starts to increase again with the number of syllables. decay, bath size of 32, and a LR scheduler that halves it For the syllabification task, including the stress markers every 5 epochs. The models are trained for 10-15 epochs. seems to outperform excluding them in most scenarios, For the task of automatic syllabification, we wanted while both accuracies achieve a peak around the 5 sylla- to check if the presence of the stress markers affects the bles mark. This result seems to align with the distribution performance of the model. Because of that, we trained of syllables in the dataset, i.e. obtaining higher scores two models: the first one was trained using the spelling for the number of syllables with more examples. For of the words with the stress markers removed, while the stress assignment errors, we also investigate the place- second one was trained with them included. ment of the predicted stressed syllable in relation with the true one (see Table 8). 95.6% of the errors misplaced Stress Assignment Errors the stressed syllable at most one position to the left, or True Predicted to the right, while almost two thirds of the erroneous bàlano balanò predictions placed the stress on the first syllable to the fèmore femòre right of the correct one. dòlmen dolmèn tùtolo tutòlo pudìco pùdico corsìa còrsia 100.0 97.5 Syllabification Errors 95.0 True Predicted Accuracy mu-o-ne muo-ne 92.5 bion-da bi-on-da 90.0 cli-en-te clien-te co-di-a-to co-dia-to 87.5 Task Stress Assignment ma-nu-brio ma-nu-bri-o 85.0 Syllabification (w/o stress markers) spa-tria-to spa-tri-a-to Syllabification (w/ stress markers) 1 2 3 4 5 6 7 8 9 10 11 Table 6 Num. Syllables Examples of erroneous test predictions provided by the deep- Figure 3: The test accuracies for each of the three tasks, learning models. computed independently on the test words, bucketed by their true number of syllables. Num. Syllables Num. Words Stress Assignment Syllabification (w/o SM) Syllabification (w/ SM) 1 721 99.03% 83.63% 84.88% 2 5,960 92.94% 96.56% 97.80% 3 23,286 94.46% 98.55% 99.19% 4 41,253 97.42% 99.03% 99.48% 5 28,357 98.92% 99.33% 99.49% 6 10,829 99.48% 99.23% 99.26% 7 3,294 99.67% 99.15% 99.15% 8 650 100.0% 99.23% 98.46% 9 132 100.0% 99.24% 99.24% 10 16 100.0% 93.75% 93.75% 11 5 100.0% 100.0% 100.0% Table 7 Similar to Figure 3 this table contains the actual values of the test accuracies for the three tasks: stress assignment, and syllabification with/without stress markers (SM) included. These scores are computed separately for words with the same number of syllables. Stressed Syllable Delta Num. Errors Pct. Errors -2 21 0.74% -1 804 28.38% 0 95 3.35% 1 1,809 63.85% 2 102 3.60% 3 2 0.07% Table 8 Starting from the incorrect predictions for stress assignment, we compute how far the assigned stress is from the actual one, in numbers of syllables (delta). A delta of −2 means that the predicted stressed syllable is the second one to the left of the correct stressed syllable. A delta of 0 in this situation means that the algorithm predicted the stressed vowel incorrectly, but the prediction sits inside the correct stressed syllable. 5. Conclusions Innovation and Digitization, CNCS/CCCDI UEFISCDI, SiRoLa project, number PN-IV-P1-PCE-2023-1701, Roma- In this paper we have investigated graphical syllabifica- nia. tion and graphical stress assignment for Italian words. 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