46 citations · 243 across the 25 of their papers we have counts for
14 papers · 1 filter
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…