46 citations · 243 across the 25 of their papers we have counts for
27 papers
Cross-Lingual Cross-Age Group Adaptation for Low-Resource Elderly Speech Emotion Recognition
Samuel Cahyawijaya, Holy Lovenia, Willy Chung +3
Speech emotion recognition plays a crucial role in human-computer interactions. However, most speech emotion recognition research is biased toward English-speaking adults, which hi…
Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study
Boxin Wang, Wei Ping, Peng Xu +9
Large decoder-only language models (LMs) can be largely improved in terms of perplexity by retrieval (e.g., RETRO), but its impact on text generation quality and downstream task ac…
RHO (): Reducing Hallucination in Open-domain Dialogues with Knowledge Grounding
Ziwei Ji, Zihan Liu, Nayeon Lee +4
Dialogue systems can leverage large pre-trained language models and knowledge to generate fluent and informative responses. However, these models are still prone to produce halluci…
Plausible May Not Be Faithful: Probing Object Hallucination in Vision-Language Pre-training
Wenliang Dai, Zihan Liu, Ziwei Ji +2
Large-scale vision-language pre-trained (VLP) models are prone to hallucinate non-existent visual objects when generating text based on visual information. In this paper, we system…
Effective Transfer Learning for Low-Resource Natural Language Understanding
Zihan Liu
Natural language understanding (NLU) is the task of semantic decoding of human languages by machines. NLU models rely heavily on large training data to ensure good performance. How…
Mere Contrastive Learning for Cross-Domain Sentiment Analysis
Yun Luo, Fang Guo, Zihan Liu +1
Cross-domain sentiment analysis aims to predict the sentiment of texts in the target domain using the model trained on the source domain to cope with the scarcity of labeled data.…
SNP2Vec: Scalable Self-Supervised Pre-Training for Genome-Wide Association Study
Samuel Cahyawijaya, Tiezheng Yu, Zihan Liu +4
Self-supervised pre-training methods have brought remarkable breakthroughs in the understanding of text, image, and speech. Recent developments in genomics has also adopted these p…
CrossNER: Evaluating Cross-Domain Named Entity Recognition
Zihan Liu, Yan Xu, Tiezheng Yu +5
Cross-domain named entity recognition (NER) models are able to cope with the scarcity issue of NER samples in target domains. However, most of the existing NER benchmarks lack doma…
Modality-Transferable Emotion Embeddings for Low-Resource Multimodal Emotion Recognition
Wenliang Dai, Zihan Liu, Tiezheng Yu +1
Despite the recent achievements made in the multi-modal emotion recognition task, two problems still exist and have not been well investigated: 1) the relationship between differen…
Cross-lingual Spoken Language Understanding with Regularized Representation Alignment
Zihan Liu, Genta Indra Winata, Peng Xu +2
Despite the promising results of current cross-lingual models for spoken language understanding systems, they still suffer from imperfect cross-lingual representation alignments be…
Learning Knowledge Bases with Parameters for Task-Oriented Dialogue Systems
Andrea Madotto, Samuel Cahyawijaya, Genta Indra Winata +4
Task-oriented dialogue systems are either modularized with separate dialogue state tracking (DST) and management steps or end-to-end trainable. In either case, the knowledge base (…
EmoGraph: Capturing Emotion Correlations using Graph Networks
Peng Xu, Zihan Liu, Genta Indra Winata +2
Most emotion recognition methods tackle the emotion understanding task by considering individual emotion independently while ignoring their fuzziness nature and the interconnection…
Language Models as Few-Shot Learner for Task-Oriented Dialogue Systems
Andrea Madotto, Zihan Liu, Zhaojiang Lin +1
Task-oriented dialogue systems use four connected modules, namely, Natural Language Understanding (NLU), a Dialogue State Tracking (DST), Dialogue Policy (DP) and Natural Language…
Meta-Transfer Learning for Code-Switched Speech Recognition
Genta Indra Winata, Samuel Cahyawijaya, Zhaojiang Lin +3
An increasing number of people in the world today speak a mixed-language as a result of being multilingual. However, building a speech recognition system for code-switching remains…
Coach: A Coarse-to-Fine Approach for Cross-domain Slot Filling
Zihan Liu, Genta Indra Winata, Peng Xu +1
As an essential task in task-oriented dialog systems, slot filling requires extensive training data in a certain domain. However, such data are not always available. Hence, cross-d…
XPersona: Evaluating Multilingual Personalized Chatbot
Zhaojiang Lin, Zihan Liu, Genta Indra Winata +5
Personalized dialogue systems are an essential step toward better human-machine interaction. Existing personalized dialogue agents rely on properly designed conversational datasets…
Kungfupanda at SemEval-2020 Task 12: BERT-Based Multi-Task Learning for Offensive Language Detection
Wenliang Dai, Tiezheng Yu, Zihan Liu +1
Nowadays, offensive content in social media has become a serious problem, and automatically detecting offensive language is an essential task. In this paper, we build an offensive…
Variational Transformers for Diverse Response Generation
Zhaojiang Lin, Genta Indra Winata, Peng Xu +2
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 tas…
Learning Fast Adaptation on Cross-Accented Speech Recognition
Genta Indra Winata, Samuel Cahyawijaya, Zihan Liu +4
Local dialects influence people to pronounce words of the same language differently from each other. The great variability and complex characteristics of accents creates a major ch…
Zero-Resource Cross-Domain Named Entity Recognition
Zihan Liu, Genta Indra Winata, Pascale Fung
Existing models for cross-domain named entity recognition (NER) rely on numerous unlabeled corpus or labeled NER training data in target domains. However, collecting data for low-r…
On the Importance of Word Order Information in Cross-lingual Sequence Labeling
Zihan Liu, Genta Indra Winata, Samuel Cahyawijaya +3
Word order variances generally exist in different languages. In this paper, we hypothesize that cross-lingual models that fit into the word order of the source language might fail…
Attention-Informed Mixed-Language Training for Zero-shot Cross-lingual Task-oriented Dialogue Systems
Zihan Liu, Genta Indra Winata, Zhaojiang Lin +2
Recently, data-driven task-oriented dialogue systems have achieved promising performance in English. However, developing dialogue systems that support low-resource languages remain…
Zero-shot Cross-lingual Dialogue Systems with Transferable Latent Variables
Zihan Liu, Jamin Shin, Yan Xu +4
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.…
Lightweight and Efficient End-to-End Speech Recognition Using Low-Rank Transformer
Genta Indra Winata, Samuel Cahyawijaya, Zhaojiang Lin +2
Highly performing deep neural networks come at the cost of computational complexity that limits their practicality for deployment on portable devices. We propose the low-rank trans…