Slot filling and intent detection are two highly correlated tasks in spoken language understanding (SLU). Recent SLU research attempts to explore zero-shot prompting techniques in large language models to alleviate the data scarcity problem. Nevertheless, the existing prompting work ignores the cross-task interaction information for SLU, which leads to sub-optimal performance. To solve this problem, we present the pioneering work of Cross-task Interactive Prompting (CroPrompt) for SLU, which enables the model to interactively leverage the information exchange across the correlated tasks in SLU. Additionally, we further introduce a multi-task self-consistency mechanism to mitigate the error propagation caused by the intent information injection. We conduct extensive experiments on the standard SLU benchmark and the results reveal that CroPrompt consistently outperforms the existing prompting approaches. In addition, the multi-task self-consistency mechanism can effectively ease the error propagation issue, thereby enhancing the performance. We hope this work can inspire more research on cross-task prompting for SLU.

Yixuan Wang, Xianzhen Luo, Fuxuan Wei, Yijun Liu, Qingfu Zhu, Xuanyu Zhang, Qing Yang, Dongliang Xu, Wanxiang Che. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

Chain-of-thought (CoT) is capable of eliciting models to explicitly generate reasoning paths, thus promoting reasoning accuracy and attracting increasing attention.Specifically, zeroshot CoT achieves remarkable improvements in a wide range of reasoning tasks by simply instructing the LLM with the prompt "Let's think step by step!".Despite the success of zero-shot CoT, the existing zero-shot prompting techniques remain limited to a single language, making it challenging to generalize to other languages and hindering global development.In this work, we introduce cross-lingual prompting (CLP), aiming to improve zero-shot CoT reasoning across languages.Specifically, CLP consists of two main components: (1) cross-lingual alignment prompting and (2) taskspecific solver prompting.The cross-lingual alignment prompting is responsible for aligning representations across different languages, whereas the task-specific solver prompting is used to generate the final chain of thoughts and results for the reasoning task.In addition, we further introduce cross-lingual self-consistent prompting (CLSP) to ensemble different reasoning paths across languages.Our experimental evaluations on several benchmarks demonstrate that CLP and CLSP significantly outperform the existing prompting methods and achieve state-of-the-art performance.We hope this work will inspire further breakthroughs in cross-lingual CoT.Answer: 48.After understanding, you should act as an expert in arithmetic reasoning in German… Verstanden! Hier ist die Lösung für die gegebene Aufgabe: 1. Da Jiesha 20 Jahre alt ist, ist Qioung 20-5... 2. Da Mary zwei Jahre jünger als Qioung ist... Antwort: 48.Sorry, I am an AI language model and I can only communicate in English.May I assist you with anything else?Let's understand the task in German step-by-step!German Prompting Please act as an expert in multi-lingual understanding in Chinese.Request:玛丽比琼小两岁，琼比杰莎大五岁。如果杰莎 20 岁，这 三个女孩的年龄总和是多少？ Let's understand the task in English step-by-step!After understanding, you should act as an expert in arithmetic reasoning in English.Let's resolve the task you understand above step-by-step!Finally, you should format your answer as 'Answer: [num]'.Sure! Let's solve the task step-by-step: 1.According to the first sentence, Mary is two years younger than Joan.Let's suppose Joan's age is J, then Mary's age is J -2. 2. According to the second sentence, Joan is five years older than Jessie.If Jessie's age is x, then Joan's age is x + 5... Answer: 68.

Spoken language understanding (SLU) has been addressed as a supervised learning problem, where a set of training data is available for each domain. However, annotating data for a new domain can be both financially costly and non-scalable. One existing approach solves the problem by conducting multi-domain learning where parameters are shared for joint training across domains, which is domain-agnostic and task-agnostic . In the article, we propose to improve the parameterization of this method by using domain-specific and task-specific model parameters for fine-grained knowledge representation and transfer. Experiments on five domains show that our model is more effective for multi-domain SLU and obtain the best results. In addition, we show its transferability when adapting to a new domain with little data, outperforming the prior best model by 12.4%. Finally, we explore the strong pre-trained model in our framework and find that the contributions from our framework do not fully overlap with contextualized word representations (RoBERTa).

Libo Qin, Fuxuan Wei, Tianbao Xie, Xiao Xu, Wanxiang Che, Ting Liu. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.