Search Results for author: Zhaojiang Lin

Found 38 papers, 26 papers with code

CAiRE in DialDoc21: Data Augmentation for Information Seeking Dialogue System

1 code implementation ACL (dialdoc) 2021 Yan Xu, Etsuko Ishii, Genta Indra Winata, Zhaojiang Lin, Andrea Madotto, Zihan Liu, Peng Xu, Pascale Fung

Information-seeking dialogue systems, including knowledge identification and response generation, aim to respond to users with fluent, coherent, and informative responses based on users’ needs, which.

Data Augmentation Response Generation

FaceFormer: Speech-Driven 3D Facial Animation with Transformers

1 code implementation CVPR 2022 Yingruo Fan, Zhaojiang Lin, Jun Saito, Wenping Wang, Taku Komura

Speech-driven 3D facial animation is challenging due to the complex geometry of human faces and the limited availability of 3D audio-visual data.

Joint Audio-Text Model for Expressive Speech-Driven 3D Facial Animation

no code implementations4 Dec 2021 Yingruo Fan, Zhaojiang Lin, Jun Saito, Wenping Wang, Taku Komura

The existing datasets are collected to cover as many different phonemes as possible instead of sentences, thus limiting the capability of the audio-based model to learn more diverse contexts.

Language Modelling

Few-Shot Bot: Prompt-Based Learning for Dialogue Systems

2 code implementations15 Oct 2021 Andrea Madotto, Zhaojiang Lin, Genta Indra Winata, Pascale Fung

A simple yet unexplored solution is prompt-based few-shot learning (Brown et al. 2020) which does not require gradient-based fine-tuning but instead uses a few examples in the LM context as the only source of learning.

Chatbot Dialogue State Tracking +2

Language Models are Few-shot Multilingual Learners

1 code implementation EMNLP (MRL) 2021 Genta Indra Winata, Andrea Madotto, Zhaojiang Lin, Rosanne Liu, Jason Yosinski, Pascale Fung

General-purpose language models have demonstrated impressive capabilities, performing on par with state-of-the-art approaches on a range of downstream natural language processing (NLP) tasks and benchmarks when inferring instructions from very few examples.

Multi-class Classification Natural Language Processing

Zero-Shot Dialogue State Tracking via Cross-Task Transfer

1 code implementation EMNLP 2021 Zhaojiang Lin, Bing Liu, Andrea Madotto, Seungwhan Moon, Paul Crook, Zhenpeng Zhou, Zhiguang Wang, Zhou Yu, Eunjoon Cho, Rajen Subba, Pascale Fung

Zero-shot transfer learning for dialogue state tracking (DST) enables us to handle a variety of task-oriented dialogue domains without the expense of collecting in-domain data.

Dialogue State Tracking Question Answering +1

CAiRE in DialDoc21: Data Augmentation for Information-Seeking Dialogue System

1 code implementation7 Jun 2021 Etsuko Ishii, Yan Xu, Genta Indra Winata, Zhaojiang Lin, Andrea Madotto, Zihan Liu, Peng Xu, Pascale Fung

Information-seeking dialogue systems, including knowledge identification and response generation, aim to respond to users with fluent, coherent, and informative responses based on users' needs, which.

Data Augmentation Response Generation

BiToD: A Bilingual Multi-Domain Dataset For Task-Oriented Dialogue Modeling

1 code implementation5 Jun 2021 Zhaojiang Lin, Andrea Madotto, Genta Indra Winata, Peng Xu, Feijun Jiang, Yuxiang Hu, Chen Shi, Pascale Fung

However, existing datasets for end-to-end ToD modeling are limited to a single language, hindering the development of robust end-to-end ToD systems for multilingual countries and regions.

Cross-Lingual Transfer Transfer Learning

Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue State Tracking

1 code implementation10 May 2021 Zhaojiang Lin, Bing Liu, Seungwhan Moon, Paul Crook, Zhenpeng Zhou, Zhiguang Wang, Zhou Yu, Andrea Madotto, Eunjoon Cho, Rajen Subba

Zero-shot cross-domain dialogue state tracking (DST) enables us to handle task-oriented dialogue in unseen domains without the expense of collecting in-domain data.

Dialogue State Tracking Transfer Learning

Continual Learning in Task-Oriented Dialogue Systems

1 code implementation EMNLP 2021 Andrea Madotto, Zhaojiang Lin, Zhenpeng Zhou, Seungwhan Moon, Paul Crook, Bing Liu, Zhou Yu, Eunjoon Cho, Zhiguang Wang

Continual learning in task-oriented dialogue systems can allow us to add new domains and functionalities through time without incurring the high cost of a whole system retraining.

Benchmark Continual Learning +3

Plug-and-Play Conversational Models

1 code implementation Findings of the Association for Computational Linguistics 2020 Andrea Madotto, Etsuko Ishii, Zhaojiang Lin, Sumanth Dathathri, Pascale Fung

These large conversational models provide little control over the generated responses, and this control is further limited in the absence of annotated conversational datasets for attribute specific generation that can be used for fine-tuning the model.

Language Modelling Response Generation

Cross-lingual Spoken Language Understanding with Regularized Representation Alignment

1 code implementation EMNLP 2020 Zihan Liu, Genta Indra Winata, Peng Xu, Zhaojiang Lin, Pascale Fung

Despite the promising results of current cross-lingual models for spoken language understanding systems, they still suffer from imperfect cross-lingual representation alignments between the source and target languages, which makes the performance sub-optimal.

Spoken Language Understanding

MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue Systems

1 code implementation EMNLP 2020 Zhaojiang Lin, Andrea Madotto, Genta Indra Winata, Pascale Fung

In this paper, we propose Minimalist Transfer Learning (MinTL) to simplify the system design process of task-oriented dialogue systems and alleviate the over-dependency on annotated data.

Dialogue State Tracking Multi-domain Dialogue State Tracking +3

The Adapter-Bot: All-In-One Controllable Conversational Model

1 code implementation28 Aug 2020 Andrea Madotto, Zhaojiang Lin, Yejin Bang, Pascale Fung

The dialogue skills can be triggered automatically via a dialogue manager, or manually, thus allowing high-level control of the generated responses.

EmoGraph: Capturing Emotion Correlations using Graph Networks

no code implementations21 Aug 2020 Peng Xu, Zihan Liu, Genta Indra Winata, Zhaojiang Lin, Pascale Fung

Most emotion recognition methods tackle the emotion understanding task by considering individual emotion independently while ignoring their fuzziness nature and the interconnections among them.

Classification Emotion Classification +3

Variational Transformers for Diverse Response Generation

2 code implementations28 Mar 2020 Zhaojiang Lin, Genta Indra Winata, Peng Xu, Zihan Liu, Pascale Fung

Despite the great promise of Transformers in many sequence modeling tasks (e. g., machine translation), their deterministic nature hinders them from generalizing to high entropy tasks such as dialogue response generation.

Machine Translation Response Generation +1

XPersona: Evaluating Multilingual Personalized Chatbot

1 code implementation EMNLP (NLP4ConvAI) 2021 Zhaojiang Lin, Zihan Liu, Genta Indra Winata, Samuel Cahyawijaya, Andrea Madotto, Yejin Bang, Etsuko Ishii, Pascale Fung

Experimental results show that the multilingual trained models outperform the translation-pipeline and that they are on par with the monolingual models, with the advantage of having a single model across multiple languages.

Chatbot Translation

Learning Fast Adaptation on Cross-Accented Speech Recognition

1 code implementation4 Mar 2020 Genta Indra Winata, Samuel Cahyawijaya, Zihan Liu, Zhaojiang Lin, Andrea Madotto, Peng Xu, Pascale Fung

The great variability and complex characteristics of accents creates a major challenge for training a robust and accent-agnostic automatic speech recognition (ASR) system.

Audio and Speech Processing Sound

On the Importance of Word Order Information in Cross-lingual Sequence Labeling

no code implementations30 Jan 2020 Zihan Liu, Genta Indra Winata, Samuel Cahyawijaya, Andrea Madotto, Zhaojiang Lin, Pascale Fung

To verify this hypothesis, we investigate whether making models insensitive to the word order of the source language can improve the adaptation performance in target languages.

named-entity-recognition Named Entity Recognition +2

Attention over Parameters for Dialogue Systems

no code implementations7 Jan 2020 Andrea Madotto, Zhaojiang Lin, Chien-Sheng Wu, Jamin Shin, Pascale Fung

Dialogue systems require a great deal of different but complementary expertise to assist, inform, and entertain humans.

Goal-Oriented Dialogue Systems

Lightweight and Efficient End-to-End Speech Recognition Using Low-Rank Transformer

no code implementations30 Oct 2019 Genta Indra Winata, Samuel Cahyawijaya, Zhaojiang Lin, Zihan Liu, Pascale Fung

Highly performing deep neural networks come at the cost of computational complexity that limits their practicality for deployment on portable devices.

speech-recognition Speech Recognition

MoEL: Mixture of Empathetic Listeners

3 code implementations IJCNLP 2019 Zhaojiang Lin, Andrea Madotto, Jamin Shin, Peng Xu, Pascale Fung

Previous research on empathetic dialogue systems has mostly focused on generating responses given certain emotions.

Getting To Know You: User Attribute Extraction from Dialogues

1 code implementation LREC 2020 Chien-Sheng Wu, Andrea Madotto, Zhaojiang Lin, Peng Xu, Pascale Fung

User attributes provide rich and useful information for user understanding, yet structured and easy-to-use attributes are often sparsely populated.

Learning Multilingual Meta-Embeddings for Code-Switching Named Entity Recognition

no code implementations WS 2019 Genta Indra Winata, Zhaojiang Lin, Pascale Fung

In this paper, we propose Multilingual Meta-Embeddings (MME), an effective method to learn multilingual representations by leveraging monolingual pre-trained embeddings.

Language Identification named-entity-recognition +1

Personalizing Dialogue Agents via Meta-Learning

1 code implementation ACL 2019 Zhaojiang Lin, Andrea Madotto, Chien-Sheng Wu, Pascale Fung

Existing personalized dialogue models use human designed persona descriptions to improve dialogue consistency.

Dialogue Generation Meta-Learning

Learning Comment Generation by Leveraging User-Generated Data

no code implementations29 Oct 2018 Zhaojiang Lin, Genta Indra Winata, Pascale Fung

Existing models on open-domain comment generation are difficult to train, and they produce repetitive and uninteresting responses.

Information Retrieval

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