5 accepted papers
Although large language models have significantly advanced natural language generation, their potential in low-resource machine translation has not yet been fully explored, especially for languages that translation models have not been trained on. In this study, we provide a detailed demonstration o…
Event forecasting requires modeling historical event data to predict future events, and achieving accurate predictions depends on effectively capturing the relevant historical information that aids forecasting. Most existing methods focus on entities and structural dependencies to capture historical
Few-shot slot tagging is an important task in dialogue systems and attracts much attention of researchers. Most previous few-shot slot tagging methods utilize meta-learning procedure for training and strive to construct a large number of different meta tasks to simulate the testing situation of insu…
There are a large number of candidate values shared among slots in multi-domain dialogue state tracking (DST). The existing span prediction-based DST methods generally adopt slot-independent value extraction architecture, which ignore the value sharing. Besides, the slot-independent design leads to…
Few-shot Intent Detection (FSID) is a challenging task in dialogue systems due to the scarcity of available annotated utterances. Although existing few-shot learning approaches have made remarkable progress, they fall short in adapting to the Generalized Few-shot Intent Detection (GFSID) task where…