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Found 12 papers, 9 papers with code
X-RiSAWOZ: High-Quality End-to-End Multilingual Dialogue Datasets and Few-shot Agents
We create a new multilingual benchmark, X-RiSAWOZ, by translating the Chinese RiSAWOZ to 4 languages: English, French, Hindi, Korean; and a code-mixed English-Hindi language.
Zero and Few-Shot Localization of Task-Oriented Dialogue Agents with a Distilled Representation
We propose automatic methods that use ToD training data in a source language to build a high-quality functioning dialogue agent in another target language that has no training data (i. e. zero-shot) or a small training set (i. e. few-shot).
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions -- training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones.
ThingTalk: An Extensible, Executable Representation Language for Task-Oriented Dialogues
Task-oriented conversational agents rely on semantic parsers to translate natural language to formal representations.
Investigating Effect of Dialogue History in Multilingual Task Oriented Dialogue Systems
Therefore, we devise an efficient and effective training solution for multilingual task-orientated dialogue systems, using the same dataset generation pipeline and end-to-end dialogue system architecture as BiToD[5], which adopted some key design choices for a minimalistic natural language design where formal dialogue states are used in place of natural language inputs.
Contextual Semantic Parsing for Multilingual Task-Oriented Dialogues
On RiSAWOZ, CrossWOZ, CrossWOZ-EN, and MultiWOZ-ZH datasets we improve the state of the art by 11%, 17%, 20%, and 0. 3% in joint goal accuracy.
Localizing Open-Ontology QA Semantic Parsers in a Day Using Machine Translation
We propose Semantic Parser Localizer (SPL), a toolkit that leverages Neural Machine Translation (NMT) systems to localize a semantic parser for a new language.
Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking
We show that data augmentation through synthesized data can improve the accuracy of zero-shot learning for both the TRADE model and the BERT-based SUMBT model on the MultiWOZ 2. 1 dataset.
HUBERT Untangles BERT to Improve Transfer across NLP Tasks
We introduce HUBERT which combines the structured-representational power of Tensor-Product Representations (TPRs) and BERT, a pre-trained bidirectional Transformer language model.
Genie: A Generator of Natural Language Semantic Parsers for Virtual Assistant Commands
We advocate formalizing the capability of virtual assistants with a Virtual Assistant Programming Language (VAPL) and using a neural semantic parser to translate natural language into VAPL code.
Clinical Parameters Prediction for Gait Disorder Recognition
Being able to predict clinical parameters in order to diagnose gait disorders in a patient is of great value in planning treatments.
Language Modeling with Generative AdversarialNetworks
Generative Adversarial Networks (GANs) have been promising in the field of image generation, however, they have been hard to train for language generation.
