## 3 Datasets  
[PERSON]'s (www.tilde.com) research interests cover morphologically complex languages, e.g.-Estonian, Latvian, Lithuanian, and Russian. Unfortunately, labeled datasets for the intent detection problems are not publicly available or may not even exist for some of these languages. The problem was overcome by taking English benchmark datasets and manually translating them into target languages. Similar datasets (having the same number of instances and intents; the same distribution of instances among training/testing subsets) contribute to the equalization of experimental conditions that, in turn, make comparative analysis possible for different languages. A detailed description of English benchmark datasets can be found in [33]: (1) the **chatbot** dataset (presented in Table 1) contains real users' questions about public transport connections; (2) **askUbuntu** (Table 2) and (3) **webapps** (Table 3) datasets are based on questions from the StackExchange ([[https://www.stackexchange.com](https://www.stackexchange.com)]([https://www.stackexchange.com](https://www.stackexchange.com))) platform. It is important to notice that training/testing splits for all these benchmark datasets were kept the same as in [33], and it is the main reason why the cross-validation has not been performed. Moreover, having several folds of the same dataset, it would be much more difficult to come up with the summarized recommendations. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Intent**} & \multirow{2}{*}{**Num. of Instances**} & \multicolumn{6}{c}{**Num. of Words**} \\ \cline{3-7}  & & **EN** & **ET** & **LT** & **LV** & **RU** \\ \hline \multicolumn{7}{c}{**Training dataset**} \\ \hline FindConnection & 57 & 510 (115) & 393 (186) & 460 (137) & 449 (145) & 482 (123) \\ \hline DepartureTime & 43 & 328 (62) & 223 (74) & 255 (59) & 245 (64) & 250 (64) \\ \hline \multicolumn{7}{c}{**Testing dataset**} \\ \hline FindConnection & 71 & 508 (99) & 369 (154) & 484 (113) & 449 (120) & 483 (104) \\ \hline DepartureTime & 35 & 241 (48) & 168 (53) & 201 (49) & 193 (55) & 192 (48) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistics about the **chatbot** dataset (numbers in brackets present numbers of distinct words). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Intent**} & \multirow{2}{*}{**Num. of Instances**} & \multicolumn{6}{c}{**Num. of Words**} \\ \cline{3-7}  & & **EN** & **ET** & **LT** & **LV** & **RU** \\ \hline \multicolumn{7}{c}{**Training dataset**} \\ \hline Make Update & 10 & 77 (42) & 59 (39) & 73 (40) & 69 (42) & 74 (40) \\ \hline None & 3 & 17 (16) & 13 (12) & 13 (12) & 13 (12) & 15 (14) \\ \hline Setup Printer & 10 & 109 (67) & 77 (57) & 85 (65) & 83 (60) & 93 (62) \\ \hline Shutdown & 13 & 96 (61) & 67 (48) & 78 (61) & 74 (53) & 87 (65) \\ \hline \multicolumn{7}{c}{Software Recommendation} & 17 & 113 (77) & 90 (66) & 104 (85) & 98 (78) & 108 (80) \\ \hline \multicolumn{7}{c}{**Testing dataset**} \\ \hline Make Update & 37 & 305 (116) & 243 (139) & 288 (117) & 283 (120) & 293 (116) \\ \hline \multicolumn{7}{c}{**Testing dataset**} \\ \hline Make Update & 37 & 305 (116) & 243 (139) & 288 (117) & 283 (120) & 293 (116) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistics about the **askUbuntu** dataset. Some language differences can already be seen directly from the tables. For example, the English language usually has the largest average number of words per instance. English is then followed by Russian, Lithuanian, and Latvian, whereas Estonian has the smallest (e.g., 510 and 393 of words covering FindConnection in the training dataset for EN and ET, respectively). If analyzing the distinct words, Estonian comes first as having the largest number, and English the last. All these observations can be explained linguistically: the English language has the least complex morphology, and different morphological forms are expressed with the help of functional words. Estonian and the other three languages are morphologically complex.