Search Results for author:
Found 9 papers, 3 papers with code
Lexicon-Based Graph Convolutional Network for Chinese Word Segmentation
Experimental results on five benchmarks and four cross-domain datasets show the lexicon-based graph convolutional network successfully captures the information of candidate words and helps to improve performance on the benchmarks (Bakeoff-2005 and CTB6) and the cross-domain datasets (SIGHAN-2010).
DUTNLP Machine Translation System for WMT21 Triangular Translation Task
This paper describes DUT-NLP Lab’s submission to the WMT-21 triangular machine translation shared task.
Summarizing Dialogues with Negative Cues
In this work, rather than directly forcing a summarization system to merely pay more attention to the salient pieces, we propose to explicitly have the model perceive the redundant parts of an input dialogue history during the training phase.
Context-Aware Word Segmentation for Chinese Real-World Discourse
This paper proposes a three-step strategy to improve the performance for discourse CWS.
Hugging Rain Man: A Novel Facial Action Units Dataset for Analyzing Atypical Facial Expressions in Children with Autism Spectrum Disorder
Children with Autism Spectrum Disorder (ASD) often exhibit atypical facial expressions.
Harnessing Webpage UIs for Text-Rich Visual Understanding
Text-rich visual understanding-the ability to process environments where dense textual content is integrated with visuals-is crucial for multimodal large language models (MLLMs) to interact effectively with structured environments.
VisualWebBench: How Far Have Multimodal LLMs Evolved in Web Page Understanding and Grounding?
Multimodal Large Language models (MLLMs) have shown promise in web-related tasks, but evaluating their performance in the web domain remains a challenge due to the lack of comprehensive benchmarks.
Topic-Aware Contrastive Learning for Abstractive Dialogue Summarization
To capture the various topic information of a conversation and outline salient facts for the captured topics, this work proposes two topic-aware contrastive learning objectives, namely coherence detection and sub-summary generation objectives, which are expected to implicitly model the topic change and handle information scattering challenges for the dialogue summarization task.
Ranked #5 on
Text Summarization
on SAMSum
