SlowGuess/NAACL_2021_OCR huggingface dataset:
Lastly, as neural models often requires large amounts of training data, we aimed to expand the dataset. We thus created a cleaned and extended dataset by Wiktionary scrapping, followed by manual validation and cleansing.
Wikitionary scraping We augment the existing dataset with a freely available resource: Wiktionary. Wiktionary entries for Latin words usually contain inflection tables, and often list the descendants in Romance languages; these descendants are, by definition, cognates. We scraped all Latin entries from Wiktionary, and extracted the forms of the daughter languages (available in the "Descendants" section). This resulted in 5,598 additional comparative entries, for a total of 22,361 new individual words. Contrary to the previous dataset, the Wiktionary-derived cognates are not based on automatic alignment between translations, but rather on direct human annotation. On
the other hand, the Wiktionary-based entries are often incomplete, and include cognates in only a subset of the daughter languages.
Form normalization Using the Wiktionaryprovided inflection tables, we decline the Latin nouns to the accusative case, and conjugate verbs to the infinitive form. We do this both to the Wiktionary-based entries and to the ones in the original dataset. We selected a sample of around 100 Latin words to check the accuracy of the automatic conjugation, against Gaffiot and Flobert (1934), finding them all correct. Finally, Latin words in the Ciobanu and Dinu (2014b) dataset for which we did not find a Wiktionary entry were conjugated "manually" by consulting (Lewis and Short, 1879; Gaffiot and Flobert, 1934).
Manual verification and cleaning After the collection of the Wikitionary dataset, we went manually through all the Latin words contained in Ciobanu and Dinu (2014b), checking them against Lewis and Short (1879); Gaffiot and Flobert (1934). Additionally, we went over the some suspicious-looking words from the daughter language and verified them against DIEZ and Donkin (1864) to ensure their etymological relatedness with the Latin source, fixing if necessary. This sort of fix was not performed systematically, but we did fix or remove around 170 words.
Finally, we sample 300 entries from the original (Ciobanu and Dinu, 2014b) dataset prior to cleaning and 300 words from our cleaned and unified version of the dataset, and manually verified them. We find 43 mistakes in the original dataset and only 4 in our version, indicating that, while still not perfect, it is of substantial higher quality.
IPA transcription To obtain the phonetic transcriptions into IPA, we utilized the transcription module of the eSpeak library, which offers transcriptions for all languages in our dataset, including Latin. While a human transcription would be preferable, a manual evaluation of 200 of the resulting transcriptions by comparing them against several sources (Allen and Allen, 1989; Hall, 1944; Deabove and Rey, 2000; Clegg and Fails, 2017; Mateus and d'Andrade, 2000; Sarlin, 2014) show high accuracy: all the 200 words were correct, except for minor systematic changes which we fixed globally to better suit the transcription to phonological conventions. Specifically, we deleted the vowel symbols  $<\upsilon>$  and  $<\mathrm{I}>$  in Italian and Romanian, which resulted in alien to
those languages, changed the sequence  $\langle \mathrm{rr} \rangle$  to  $\langle \mathrm{r} \rangle$  in Spanish, and regularized the Portuguese transcriptions, which showed some phonological traits of Brazilian Portuguese.
Final dataset The resulting dataset, used for all experiments in this work, contains 8,799 entries. The dataset was randomly split into train, evaluation and test sets, with 7,038 examples  $(80\%)$  used for training, 703  $(8\%)$  for evaluation and 1,055  $(12\%)$  for testing.
Overall, the dataset contains 41,563 distinct words across the different languages (for a total of 83,126 words counting both the orthographic and the phonetic datasets), with 7,384 Italian words, 7,183 Spanish words, 6,806 Portuguese words, 6,505 French words and 4,886 Romanian words. As vowel lengths were found to be difficult to recover, we created the following variations of the dataset: with and without vowel length (for both the orthographic and phonetic datasets), and without a contrast (for the phonetic dataset).
A.2 Phoneme representations
In this appendix, we show the hierarchical clustering created for all the languages in our dataset. As it can be noted from figure 5, the results for the different languages exhibit representations similar to those found in the French clustering: the primary division in each language is between vowels and consonants. In Portuguese, Latin, Spanish and Romanian some consonants are grouped together with vowels. These consonants are restricted to nasals, liquids or glides. The inclusion of these consonants can be explained by the peculiarity of their nature: all of them have a special phonological status, displaying similarities in their behavior to vowels. In all languages phonologically related phonemes tend to be group under the same nodes. Among the others, glides are either found together with each other (as in French, Italian and Romanian) or with their vocalic counterparts (Latin, Spanish and Portuguese), consonants differentiated only in voicing are usually paired ([ʃ] and [3]), front and back vowels forms clusters and allophones usually shares the same node (Italian, French, Romanian, Spanish, Portuguese).