149 citations · 177 across the 19 of their papers we have counts for
15 papers · 1 filter
On the Robustness of Question Rewriting Systems to Questions of Varying Hardness
Hai Ye, Hwee Tou Ng, Wenjuan Han
In conversational question answering (CQA), the task of question rewriting~(QR) in context aims to rewrite a context-dependent question into an equivalent self-contained question t…
CollabKG: A Learnable Human-Machine-Cooperative Information Extraction Toolkit for (Event) Knowledge Graph Construction
Xiang Wei, Yufeng Chen, Ning Cheng +3
In order to construct or extend entity-centric and event-centric knowledge graphs (KG and EKG), the information extraction (IE) annotation toolkit is essential. However, existing I…
Human-in-the-Loop through Chain-of-Thought
Zefan Cai, Baobao Chang, Wenjuan Han
While the emergence of powerful language models along with Chain-of-thought prompting has made automation more and more omnipresent, it sometimes demonstrates its weakness in long-…
Enhance Reasoning Ability of Visual-Language Models via Large Language Models
Yueting Yang, Xintong Zhang, Wenjuan Han
Pre-trained visual language models (VLM) have shown excellent performance in image caption tasks. However, it sometimes shows insufficient reasoning ability. In contrast, large lan…
Towards Understanding and Improving Knowledge Distillation for Neural Machine Translation
Songming Zhang, Yunlong Liang, Shuaibo Wang +4
Knowledge distillation (KD) is a promising technique for model compression in neural machine translation. However, where the knowledge hides in KD is still not clear, which may hin…
ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT
Xiang Wei, Xingyu Cui, Ning Cheng +9
Zero-shot information extraction (IE) aims to build IE systems from the unannotated text. It is challenging due to involving little human intervention. Challenging but worthwhile,…
Modeling Instance Interactions for Joint Information Extraction with Neural High-Order Conditional Random Field
Zixia Jia, Zhaohui Yan, Wenjuan Han +2
Prior works on joint Information Extraction (IE) typically model instance (e.g., event triggers, entities, roles, relations) interactions by representation enhancement, type depend…
Diversity-Driven Combination for Grammatical Error Correction
Wenjuan Han, Hwee Tou Ng
Grammatical error correction (GEC) is the task of detecting and correcting errors in a written text. The idea of combining multiple system outputs has been successfully used in GEC…
Robust Transfer Learning with Pretrained Language Models through Adapters
Wenjuan Han, Bo Pang, Yingnian Wu
Transfer learning with large pretrained transformer-based language models like BERT has become a dominating approach for most NLP tasks. Simply fine-tuning those large language mod…
Constrained Text Generation with Global Guidance -- Case Study on CommonGen
Yixian Liu, Liwen Zhang, Wenjuan Han +2
This paper studies constrained text generation, which is to generate sentences under certain pre-conditions. We focus on CommonGen, the task of generating text based on a set of co…
Second-Order Unsupervised Neural Dependency Parsing
Songlin Yang, Yong Jiang, Wenjuan Han +1
Most of the unsupervised dependency parsers are based on first-order probabilistic generative models that only consider local parent-child information. Inspired by second-order sup…
Adversarial Attack and Defense of Structured Prediction Models
Wenjuan Han, Liwen Zhang, Yong Jiang +1
Building an effective adversarial attacker and elaborating on countermeasures for adversarial attacks for natural language processing (NLP) have attracted a lot of research in rece…
A Survey of Unsupervised Dependency Parsing
Wenjuan Han, Yong Jiang, Hwee Tou Ng +1
Syntactic dependency parsing is an important task in natural language processing. Unsupervised dependency parsing aims to learn a dependency parser from sentences that have no anno…
Combining Generative and Discriminative Approaches to Unsupervised Dependency Parsing via Dual Decomposition
Yong Jiang, Wenjuan Han, Kewei Tu
Unsupervised dependency parsing aims to learn a dependency parser from unannotated sentences. Existing work focuses on either learning generative models using the expectation-maxim…
Dependency Grammar Induction with Neural Lexicalization and Big Training Data
Wenjuan Han, Yong Jiang, Kewei Tu
We study the impact of big models (in terms of the degree of lexicalization) and big data (in terms of the training corpus size) on dependency grammar induction. We experimented wi…