LaDA: Latent Dialogue Action For Zero-shot Cross-lingual Neural Network Language Modeling
Cross-lingual adaptation has proven effective in spoken language understanding (SLU) systems with limited resources. Existing methods are frequently unsatisfactory for intent detection and slot filling, particularly for distant languages that differ significantly from the source language in scripts, morphology, and syntax. Latent Dialogue Action (LaDA) layer is proposed to optimize decoding strategy in order to address the aforementioned issues. The model consists of an additional layer of latent dialogue action. It enables our model to improve a system's capability of handling conversations with complex multilingual intent and slot values of distant languages. To the best of our knowledge, this is the first exhaustive investigation of the use of latent variables for optimizing cross-lingual SLU policy during the decode stage. LaDA obtains state-of-the-art results on public datasets for both zero-shot and few-shot adaptation.
Code (0)
등록된 구현이 없습니다.
Tasks
Intent DetectionLanguage ModelingLanguage Modellingslot-fillingSlot FillingSpoken Language UnderstandingSimilar Papers 제목 키워드 기반
DiactTOD: Learning Generalizable Latent Dialogue Acts for Controllable Task-Oriented Dialogue Systems
Dialogue act annotations are important to improve response generation quality in task-oriented dialogue systems. However, it can be challenging to use dialogue acts to control response generation in a generalizable way b…
Response GenerationTask-Oriented Dialogue SystemsDriving Everywhere with Large Language Model Policy Adaptation
Adapting driving behavior to new environments, customs, and laws is a long-standing problem in autonomous driving, precluding the widespread deployment of autonomous vehicles (AVs). In this paper, we present LLaDA, a sim…
Autonomous DrivingAutonomous VehiclesLanguage ModelingLanguage Modelling+3Zero-shot Cross-lingual Dialogue Systems with Transferable Latent Variables
Despite the surging demands for multilingual task-oriented dialog systems (e.g., Alexa, Google Home), there has been less research done in multilingual or cross-lingual scenarios. Hence, we propose a zero-shot adaptation…
Intent DetectionNatural Language Understandingslot-fillingSlot FillingInstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning
Instruction tuning is an emergent paradigm in NLP wherein natural language instructions are leveraged with language models to induce zero-shot performance on unseen tasks. Instructions have been shown to enable good perf…
Dialogue EvaluationDialogue GenerationIntent DetectionNatural Language Understanding+2Zero-Shot Dialogue Relation Extraction by Relating Explainable Triggers and Relation Names
Developing dialogue relation extraction (DRE) systems often requires a large amount of labeled data, which can be costly and time-consuming to annotate. In order to improve scalability and support diverse, unseen relatio…
RelationRelation Extraction