H i TZ at V ar D ial 2025 N or SID : Overcoming Data Scarcity with Language Transfer and Automatic Data Annotation (2025)

Abstract: In this paper we present our submission for the NorSID Shared Task as part of the 2025 VarDial Workshop, consisting of three tasks: Intent Detection, Slot Filling and Dialect Identification, evaluated using data in different dialects of the Norwegian language. For Intent Detection and Slot Filling, we have fine-tuned a multitask model in a cross-lingual setting, to leverage the xSID dataset available in 17 languages. In the case of Dialect Identification, our final submission consists of a model fine-tuned on the provided development set, which has obtained the highest scores within our experiments. Our final results on the test set show that our models do not drop in performance compared to the development set, likely due to the domain-specificity of the dataset and the similar distribution of both subsets. Finally, we also report an in-depth analysis of the provided datasets and their artifacts, as well as other sets of experiments that have been carried out but did not yield the best results. Additionally, we present an analysis on the reasons why some methods have been more successful than others; mainly the impact of the combination of languages and domain-specificity of the training data on the results.

## introduction dialectal variation is ubiquitous in human language and should be taken into account when performing natural language processing (nlp) tasks, as nlp systems unable to deal with dialectal data can cause users to feel frustrated and lead to unintended biases @xcite . this is especially relevant for spoken language understanding (slu), a field of speech processing and natural language understanding aimed at ensuring the semantic comprehension of human utterances by virtual assistants. to make systems that rely on slu more robust and able to handle real use-cases, it is necessary to develop resources for these tasks not only for different languages, but for different language varieties, so that the benefits of these models can reach a wider variety of speech communities. with this motivation, the norsid shared task consists of three subtasks (intent detection, slot filling and dialect identification) in four norwegian variants: bokmål (b), western (v), trøndersk (t) and north norwegian (n). the tasks are centered around common virtual assistant tasks, such as setting alarms or questions about the weather. our team participated in all three subtasks, for a total of 6 runs: 3 for the sid (slot and intent detection) tasks and 3 for dialect identification. as a team, we placed first in dialect identification, second in intent detection, and third in slot filling. our code is publicly available on github. foot_0 2 task descriptions as mentioned, this shared task consists of the following three subtasks: intent detection. it is a text classification task that assigns intent labels to the utterances of the users, to guide the chatbot's answer, depending on its domain and purpose. slot filling. it requires classifying token spans that contain relevant information for a virtual assistant to fulfill certain tasks, e.g., to set an alarm, the assistant needs to know the time to set it to. ## intent detection & slot filling in this section, we will detail our participation in the intent and slot filling subtasks. we first explain the data (section 3.1) and the experimental design (section 3.2), and finally a description and an analysis of our results (section 3.3). ## dialect identification in this section, we will present the dialect identification task, starting with the data used in our experiments (section 4.1), followed by the experimental setting training only on the development set from the shared task (section 4.2), as well as the experimental settings when training on alternative sources of data (section 4.3). finally, we describe the results of using different data and settings (section 4.4). ## conclusion and future work we have presented our submission for the norsid shared task in the 2025 vardial workshop @xcite . we have participated in the three proposed tasks -intent detection, slot filling and dialect identification -with 3 submissions for each of them. for the intent detection & slot filling tasks we designed a multitask model, improving efficiency with respect to having a model for each task. additionally, as both tasks are highly related, this combination improves the performance of the model in both tasks to 97.69% accuracy and 85.37% f 1 , respectively, in the test set. in dialect identification, we tested many different approaches by using the development data as training, as well as additional data from tweets and transcriptions. however, none of the settings we tried were able to surpass the performance of norbert3-l fine-tuned only on the development set, which achieved 84.17 f 1 on the test set. the research presented in this paper has opened the way to many questions that need further investigation. we believe that the results could be improved using better encoder, e.g., deberta @xcite , and decoder, e.g., llama 3.1 70b) models. the additional data we collected for dialect identification has not been successful due to the narrow domain of the tasks, but it is likely that for other tasks with a stronger domain shift this data could provide for more robust training.