38 citations · 118 across the 22 of their papers we have counts for
15 papers · 1 filter
Large Language Models Are Also Good Prototypical Commonsense Reasoners
Chenin Li, Qianglong Chen, Yin Zhang +2
Commonsense reasoning is a pivotal skill for large language models, yet it presents persistent challenges in specific tasks requiring this competence. Traditional fine-tuning appro…
Layout and Task Aware Instruction Prompt for Zero-shot Document Image Question Answering
Wenjin Wang, Yunhao Li, Yixin Ou +1
Layout-aware pre-trained models has achieved significant progress on document image question answering. They introduce extra learnable modules into existing language models to capt…
WYWEB: A NLP Evaluation Benchmark For Classical Chinese
Bo Zhou, Qianglong Chen, Tianyu Wang +2
To fully evaluate the overall performance of different NLP models in a given domain, many evaluation benchmarks are proposed, such as GLUE, SuperGLUE and CLUE. The fi eld of natura…
Distinguish Before Answer: Generating Contrastive Explanation as Knowledge for Commonsense Question Answering
Qianglong Chen, Guohai Xu, Ming Yan +4
Existing knowledge-enhanced methods have achieved remarkable results in certain QA tasks via obtaining diverse knowledge from different knowledge bases. However, limited by the pro…
AMTSS: An Adaptive Multi-Teacher Single-Student Knowledge Distillation Framework For Multilingual Language Inference
Qianglong Chen, Feng Ji, Feng-Lin Li +4
Knowledge distillation is of key importance to launching multilingual pre-trained language models for real applications. To support cost-effective language inference in multilingua…
ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding
Qiming Peng, Yinxu Pan, Wenjin Wang +12
Recent years have witnessed the rise and success of pre-training techniques in visually-rich document understanding. However, most existing methods lack the systematic mining and u…
DAMO-NLP at NLPCC-2022 Task 2: Knowledge Enhanced Robust NER for Speech Entity Linking
Shen Huang, Yuchen Zhai, Xinwei Long +4
Speech Entity Linking aims to recognize and disambiguate named entities in spoken languages. Conventional methods suffer gravely from the unfettered speech styles and the noisy tra…
DictBERT: Dictionary Description Knowledge Enhanced Language Model Pre-training via Contrastive Learning
Qianglong Chen, Feng-Lin Li, Guohai Xu +3
Although pre-trained language models (PLMs) have achieved state-of-the-art performance on various natural language processing (NLP) tasks, they are shown to be lacking in knowledge…
Rethinking the Value of Gazetteer in Chinese Named Entity Recognition
Qianglong Chen, Xiangji Zeng, Jiangang Zhu +4
Gazetteer is widely used in Chinese named entity recognition (NER) to enhance span boundary detection and type classification. However, to further understand the generalizability a…
Dialogue State Tracking with Multi-Level Fusion of Predicted Dialogue States and Conversations
Jingyao Zhou, Haipang Wu, Zehao Lin +2
Most recently proposed approaches in dialogue state tracking (DST) leverage the context and the last dialogue states to track current dialogue states, which are often slot-value pa…
Improving Commonsense Question Answering by Graph-based Iterative Retrieval over Multiple Knowledge Sources
Qianglong Chen, Feng Ji, Haiqing Chen +1
In order to facilitate natural language understanding, the key is to engage commonsense or background knowledge. However, how to engage commonsense effectively in question answerin…
MTSS: Learn from Multiple Domain Teachers and Become a Multi-domain Dialogue Expert
Shuke Peng, Feng Ji, Zehao Lin +3
How to build a high-quality multi-domain dialogue system is a challenging work due to its complicated and entangled dialogue state space among each domain, which seriously limits t…
Predict-then-Decide: A Predictive Approach for Wait or Answer Task in Dialogue Systems
Zehao Lin, Shaobo Cui, Guodun Li +6
Different people have different habits of describing their intents in conversations. Some people tend to deliberate their intents in several successive utterances, i.e., they use s…
Task-Oriented Conversation Generation Using Heterogeneous Memory Networks
Zehao Lin, Xinjing Huang, Feng Ji +2
How to incorporate external knowledge into a neural dialogue model is critically important for dialogue systems to behave like real humans. To handle this problem, memory networks…
Teacher-Student Framework Enhanced Multi-domain Dialogue Generation
Shuke Peng, Xinjing Huang, Zehao Lin +3
Dialogue systems dealing with multi-domain tasks are highly required. How to record the state remains a key problem in a task-oriented dialogue system. Normally we use human-define…