31 citations · 129 across the 17 of their papers we have counts for
19 papers
Efficient Prediction of Peptide Self-assembly through Sequential and Graphical Encoding
Zihan Liu, Jiaqi Wang, Yun Luo +3
In recent years, there has been an explosion of research on the application of deep learning to the prediction of various peptide properties, due to the significant development and…
Improving (Dis)agreement Detection with Inductive Social Relation Information From Comment-Reply Interactions
Yun Luo, Zihan Liu, Stan Z. Li +1
(Dis)agreement detection aims to identify the authors' attitudes or positions (\textit{agree, disagree, neutral}) towards a specific text. It is limited for existing methods merely…
Re-ViLM: Retrieval-Augmented Visual Language Model for Zero and Few-Shot Image Captioning
Zhuolin Yang, Wei Ping, Zihan Liu +13
Augmenting pretrained language models (LMs) with a vision encoder (e.g., Flamingo) has obtained the state-of-the-art results in image-to-text generation. However, these models stor…
What Does the Gradient Tell When Attacking the Graph Structure
Zihan Liu, Ge Wang, Yun Luo +1
Recent research has revealed that Graph Neural Networks (GNNs) are susceptible to adversarial attacks targeting the graph structure. A malicious attacker can manipulate a limited n…
Exploiting Sentiment and Common Sense for Zero-shot Stance Detection
Yun Luo, Zihan Liu, Yuefeng Shi +2
The stance detection task aims to classify the stance toward given documents and topics. Since the topics can be implicit in documents and unseen in training data for zero-shot set…
Multi-Stage Prompting for Knowledgeable Dialogue Generation
Zihan Liu, Mostofa Patwary, Ryan Prenger +4
Existing knowledge-grounded dialogue systems typically use finetuned versions of a pretrained language model (LM) and large-scale knowledge bases. These models typically fail to ge…
ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation
Holy Lovenia, Samuel Cahyawijaya, Genta Indra Winata +11
Code-switching is a speech phenomenon occurring when a speaker switches language during a conversation. Despite the spontaneous nature of code-switching in conversational spoken la…
NER-BERT: A Pre-trained Model for Low-Resource Entity Tagging
Zihan Liu, Feijun Jiang, Yuxiang Hu +2
Named entity recognition (NER) models generally perform poorly when large training datasets are unavailable for low-resource domains. Recently, pre-training a large-scale language…
Boosting Discriminative Visual Representation Learning with Scenario-Agnostic Mixup
Siyuan Li, Zicheng Liu, Zedong Wang +3
Mixup is a well-known data-dependent augmentation technique for DNNs, consisting of two sub-tasks: mixup generation and classification. However, the recent dominant online training…
Vision Guided Generative Pre-trained Language Models for Multimodal Abstractive Summarization
Tiezheng Yu, Wenliang Dai, Zihan Liu +1
Multimodal abstractive summarization (MAS) models that summarize videos (vision modality) and their corresponding transcripts (text modality) are able to extract the essential info…
Surrogate Representation Learning with Isometric Mapping for Gray-box Graph Adversarial Attacks
Zihan Liu, Yun Luo, Zelin Zang +1
Gray-box graph attacks aim at disrupting the performance of the victim model by using inconspicuous attacks with limited knowledge of the victim model. The parameters of the victim…
CAiRE in DialDoc21: Data Augmentation for Information-Seeking Dialogue System
Etsuko Ishii, Yan Xu, Genta Indra Winata +5
Information-seeking dialogue systems, including knowledge identification and response generation, aim to respond to users with fluent, coherent, and informative responses based on…
X2Parser: Cross-Lingual and Cross-Domain Framework for Task-Oriented Compositional Semantic Parsing
Zihan Liu, Genta Indra Winata, Peng Xu +1
Task-oriented compositional semantic parsing (TCSP) handles complex nested user queries and serves as an essential component of virtual assistants. Current TCSP models rely on nume…
Continual Mixed-Language Pre-Training for Extremely Low-Resource Neural Machine Translation
Zihan Liu, Genta Indra Winata, Pascale Fung
The data scarcity in low-resource languages has become a bottleneck to building robust neural machine translation systems. Fine-tuning a multilingual pre-trained model (e.g., mBART…
Retrieval-Free Knowledge-Grounded Dialogue Response Generation with Adapters
Yan Xu, Etsuko Ishii, Samuel Cahyawijaya +5
To diversify and enrich generated dialogue responses, knowledge-grounded dialogue has been investigated in recent years. The existing methods tackle the knowledge grounding challen…
AdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarization
Tiezheng Yu, Zihan Liu, Pascale Fung
State-of-the-art abstractive summarization models generally rely on extensive labeled data, which lowers their generalization ability on domains where such data are not available.…
Are Multilingual Models Effective in Code-Switching?
Genta Indra Winata, Samuel Cahyawijaya, Zihan Liu +3
Multilingual language models have shown decent performance in multilingual and cross-lingual natural language understanding tasks. However, the power of these multilingual models i…
Multimodal End-to-End Sparse Model for Emotion Recognition
Wenliang Dai, Samuel Cahyawijaya, Zihan Liu +1
Existing works on multimodal affective computing tasks, such as emotion recognition, generally adopt a two-phase pipeline, first extracting feature representations for each single…
Exploring Fine-tuning Techniques for Pre-trained Cross-lingual Models via Continual Learning
Zihan Liu, Genta Indra Winata, Andrea Madotto +1
Recently, fine-tuning pre-trained language models (e.g., multilingual BERT) to downstream cross-lingual tasks has shown promising results. However, the fine-tuning process inevitab…