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Found 12 papers, 1 papers with code
Data Augmentation and Learned Layer Aggregation for Improved Multilingual Language Understanding in Dialogue
Scaling dialogue systems to a multitude of domains, tasks and languages relies on costly and time-consuming data annotation for different domain-task-language configurations.
Natural Language Processing for Multilingual Task-Oriented Dialogue
In this tutorial, we will thus discuss and demonstrate the importance of (building) multilingual ToD systems, and then provide a systematic overview of current research gaps, challenges and initiatives related to multilingual ToD systems, with a particular focus on their connections to current research and challenges in multilingual and low-resource NLP.
Analyzing and Adapting Large Language Models for Few-Shot Multilingual NLU: Are We There Yet?
Supervised fine-tuning (SFT), supervised instruction tuning (SIT) and in-context learning (ICL) are three alternative, de facto standard approaches to few-shot learning.
$\textit{Dial BeInfo for Faithfulness}$: Improving Factuality of Information-Seeking Dialogue via Behavioural Fine-Tuning
Factuality is a crucial requirement in information seeking dialogue: the system should respond to the user's queries so that the responses are meaningful and aligned with the knowledge provided to the system.
SQATIN: Supervised Instruction Tuning Meets Question Answering for Improved Dialogue NLU
Task-oriented dialogue (ToD) systems help users execute well-defined tasks across a variety of domains (e. g., $\textit{flight booking}$ or $\textit{food ordering}$), with their Natural Language Understanding (NLU) components being dedicated to the analysis of user utterances, predicting users' intents ($\textit{Intent Detection}$, ID) and extracting values for informational slots ($\textit{Value Extraction}$, VE).
Transfer-Free Data-Efficient Multilingual Slot Labeling
It is especially effective for the most challenging transfer-free few-shot setups, paving the way for quick and data-efficient bootstrapping of multilingual slot labelers for ToD.
Little Red Riding Hood Goes Around the Globe:Crosslingual Story Planning and Generation with Large Language Models
Previous work has demonstrated the effectiveness of planning for story generation exclusively in a monolingual setting focusing primarily on English.
MULTI3NLU++: A Multilingual, Multi-Intent, Multi-Domain Dataset for Natural Language Understanding in Task-Oriented Dialogue
We use MULTI3NLU++ to benchmark state-of-the-art multilingual models for the NLU tasks of intent detection and slot labelling for TOD systems in the multilingual setting.
Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation
Through this process we annotate a new large-scale dataset for training and evaluation of multilingual and cross-lingual ToD systems.
Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems
We find that the most critical factor preventing the creation of truly multilingual ToD systems is the lack of datasets in most languages for both training and evaluation.
Pretraining Methods for Dialog Context Representation Learning
This paper examines various unsupervised pretraining objectives for learning dialog context representations.
Beyond Turing: Intelligent Agents Centered on the User
Most research on intelligent agents centers on the agent and not on the user.
