# Automatic Simplification of Lithuanian Administrative Texts
## 3 Data  
### Data for Fine-Tuning  
To explore the effect of data in terms of model performance, we used two datasets, see Table 1:  
* **Parallel Corpus 1**--a dataset where each original sentence had one simplified equivalent;
* **Parallel Corpus 2**--a dataset where Parallel Corpus 1 was complemented with additional sentences, and part of them had more than one simplified equivalent (two to three), following such text simplification corpora as SimPA [62] and Human Simplification with Sentence Fusion Data Set (HSSF) [63]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Parallel Corpus 1**} & \multicolumn{2}{c}{**Parallel Corpus 2**} \\ \hline  & **Original** & **Simplified** & **Original** & **Simplified** \\  & **Sentences** & **Sentences** & **Sentences** \\ \hline Number of sentences & 2142 & 2521 & 3123 & 2999 \\ \hline Number of words & 36,404 & 34,702 & 64,936 & 52,382 \\ \hline Average sentence length & 14.75 & 12.43 & 16.97 & 13.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Basic statistics of corpora. **Simplification guidelines. ** We aim at simplifying Lithuanian administrative texts to a Plain Language level [4] to make them more easily understood by the general public. Therefore, lexical and syntactic simplification rules that were applied in preparing our Parallel Corpus 1 and Parallel Corpus 2 were mainly derived from cross-linguistic Plain Language principles [64; 65]. Also, where applicable, we took into account text simplification rules from languages with similar grammatical structures to Lithuanian [66; 67]. Additionally, we formulated Lithuanian-specific rules, particularly for handling particles. Therefore, guidelines for Plain Lithuanian feature three levels of simplification operations and can be summarized as follows:  
1. **Paragraph-level simplification. ** There are two main rules in this group: 1. [label=()]
2. Sentence shortening: sentences longer than 12 words should be divided into smaller units, preferably by converting embedded relative clauses into independent sentences. 2. List creation: If there are more than two coordinated elements (object or subject noun phrases, clauses, etc.) with a homogenous function in a sentence, they should be transformed into vertical lists. 3. **Lexical-level simplification. ** There are three main rules: 1. [label=()]
4. Prioritizing the use of more frequent synonyms, as determined by the Lithuanian frequency dictionary [68], disregarding conventional formal register requirements. 2. Avoiding metaphors and uncommon acronyms. 3. Defining obscure terms in separate sentences. 5. **Syntactic-level simplification. ** Key simplification strategies include: 1. [label=()]
6. Transforming passive voice constructions into active voice. 2. Replacing active participle and gerund constructions with relative clauses. 3. Minimizing the use of nominalizations. 4. Favoring affirmative sentences over negations. 5. Introducing demonstrative pronouns and determiners to enhance clarity where appropriate. **Parallel Corpus 1. ** This dataset comprises 2142 entries with two columns, where the first column contains original sentences or text fragments, equivalent to sentences, while the second column contains manually simplified versions of the corresponding original content. The original sentences were collected manually from a series of Lithuanian public institutions' websites, selected with the aim to capture a variety of topics (healthcare, security, immigration, social services, etc.). All data were simplified by four experts according to guidelines of a simplified version of the language, intended for non-specialists (general public) [11] and described above. The data sources for this dataset were various Lithuanian governmental and non-governmental public institution websites that provide information on services such as social benefits, migration, utilities, copyright, and other issues.