Search Results for author: Mehrad Moradshahi

Found 12 papers, 9 papers with code

Zero and Few-Shot Localization of Task-Oriented Dialogue Agents with a Distilled Representation

1 code implementation18 Feb 2023 Mehrad Moradshahi, Sina J. Semnani, Monica S. Lam

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).

Dialogue State Tracking Machine Translation +1

ThingTalk: An Extensible, Executable Representation Language for Task-Oriented Dialogues

1 code implementation23 Mar 2022 Monica S. Lam, Giovanni Campagna, Mehrad Moradshahi, Sina J. Semnani, Silei Xu

Task-oriented conversational agents rely on semantic parsers to translate natural language to formal representations.

Semantic Parsing

Investigating Effect of Dialogue History in Multilingual Task Oriented Dialogue Systems

no code implementations23 Dec 2021 Michael Sun, Kaili Huang, Mehrad Moradshahi

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.

Dialogue State Tracking Task-Oriented Dialogue Systems

Contextual Semantic Parsing for Multilingual Task-Oriented Dialogues

1 code implementation4 Nov 2021 Mehrad Moradshahi, Victoria Tsai, Giovanni Campagna, Monica S. Lam

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.

Dialogue State Tracking Machine Translation +3

Localizing Open-Ontology QA Semantic Parsers in a Day Using Machine Translation

1 code implementation EMNLP 2020 Mehrad Moradshahi, Giovanni Campagna, Sina J. Semnani, Silei Xu, Monica S. Lam

We propose Semantic Parser Localizer (SPL), a toolkit that leverages Neural Machine Translation (NMT) systems to localize a semantic parser for a new language.

Machine Translation NMT +3

Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking

1 code implementation ACL 2020 Giovanni Campagna, Agata Foryciarz, Mehrad Moradshahi, Monica S. Lam

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.

Data Augmentation Dialogue State Tracking +3

HUBERT Untangles BERT to Improve Transfer across NLP Tasks

1 code implementation25 Oct 2019 Mehrad Moradshahi, Hamid Palangi, Monica S. Lam, Paul Smolensky, Jianfeng Gao

We introduce HUBERT which combines the structured-representational power of Tensor-Product Representations (TPRs) and BERT, a pre-trained bidirectional Transformer language model.

Language Modelling

Genie: A Generator of Natural Language Semantic Parsers for Virtual Assistant Commands

1 code implementation18 Apr 2019 Giovanni Campagna, Silei Xu, Mehrad Moradshahi, Richard Socher, Monica S. Lam

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.

Data Augmentation Translation

Clinical Parameters Prediction for Gait Disorder Recognition

no code implementations22 May 2018 Soheil Esmaeilzadeh, Ouassim Khebzegga, Mehrad Moradshahi

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

no code implementations8 Apr 2018 Mehrad Moradshahi, Utkarsh Contractor

Generative Adversarial Networks (GANs) have been promising in the field of image generation, however, they have been hard to train for language generation.

Image Generation Language Modelling +1

Cannot find the paper you are looking for? You can Submit a new open access paper.