LTG at V ar D ial 2025 N or SID : More and Better Training Data for Slot and Intent Detection (2025)

Abstract: This paper describes the LTG submission to the VarDial 2025 shared task, where we participate in the Norwegian slot and intent detection subtasks. The shared task focuses on Norwegian dialects, which present challenges due to their low-resource nature and variation. We test a variety of neural models and training data configurations, with the focus on improving and extending the available Norwegian training data. This includes automatically re-aligning slot spans in Norwegian Bokmål, as well as re-translating the original English training data into both Bokmål and Nynorsk. % to address dialectal diversity. We also re-annotate an external Norwegian dataset to augment the training data. Our best models achieve first place in both subtasks, achieving an span F1 score of 0.893 for slot filling and an accuracy of 0.980 for intent detection. Our results indicate that while translation quality is less critical, improving the slot labels has a notable impact on slot performance. Moreover, adding more standard Norwegian data improves performance, but incorporating even small amounts of dialectal data leads to greater gains.

## introduction the task of spoken language understanding (slu) is an essential part of task-oriented dialogue systems and voice assistants like siri and alexa. slu consists in annotating and identifying the meaning of spoken prompts, and typically comprise an automatic speech recognition (asr) component for converting audio to text, alongside a natural language understanding (nlu) component for extracting the semantic meaning of the utterance @xcite . slot and intent detection (sid), also known as slot filling and intent classification, is a key task in nlu. intent classification categorizes an entire user utterance into a predefined intent class, determining the purpose or goal behind the user's utterance. on the other hand, slot filling is a span ## existing data and pre-trained models in recent years, jointly addressing the tasks of slot and intent detection has been recognized as an effective strategy @xcite . in this work, we adopt this joint approach by utilizing the machamp 0.4.2 toolkit (van der @xcite with default hyperparameters for all experiments. we evaluate the models on the norsid development set, and compare results to the results of mbert @xcite , following the setup in van der @xcite . ## improving and extending the training data to address the lower span f1 scores observed when fine-tuning on norwegian data, we further explore ways to improving the quality of the training data. ## our shared task submission for our submission, we selected the bestperforming model and training data combination per subtask. for slot filling, our best-performing model is nb-bert fine-tuned on english, while for intent detection, it is norbert fine-tuned on en+nb_rt. however, due to technical difficulties in test set prediction with norbert, we submitted our second-best intent detection model, namely nb-bert fine-tuned on en+nb_ra, whose performance is nearly identical. the shared task guidelines allowed the participants to use the development set for training. in order to further enhance the models mentioned above, we fine-tune them with the inclusion of the norsid dev set. since this prevents us from using a validation set, we submitted models after 20 epochs and after the best epoch. this resulted in three systems per subtask. ## conclusion in this paper, we presented our contribution to the two subtasks of the vardial 2025 shared task: intent detection and slot filling. we evaluated different pre-trained models, including nb-bert, xlm-r, and norbert, and identified nb-bert as the best overall model, likely due to its superior ability to handle the linguistic complexities of norwegian language varieties. slot filling emerged as a more challenging task than intent detection, with the latter showing consistent accuracy across experiments. this consistency can be attributed to the ease of transferring intents from the original english xsid 0.6 to our different norwegian versions, unlike slots, which rely on an automatic alignment process prone to errors. our efforts to enhance slot annotations did not achieve the same level of performance as fine-tuning exclusively on english data, highlighting the critical role of high-quality slot annotations and the necessity for further refinement. in addition, intent detection operates at the sentence level, relying on broader semantic features rather than the token-level distinctions critical for slot filling. as a result, it is less sensitive to dialectal variation and does not require extensive dialect-specific data. models fine-tuned solely on bokmål performed comparably to those incorporating dialectal data for intent detection. in contrast, slot filling is highly dependent on dialect-specific data due to token-level linguistic intricacies. for dialects, adding dialect-specific data proved more impactful than merely increasing the amount of bokmål data. looking ahead, we aim to experiment with the inclusion of raw dialect data to better capture linguistic variation at the token level. additionally, we intend to explore alternative methods for aligning slots between english and norwegian to further enhance the quality of slot annotations.