27 citations · 82 across the 30 of their papers we have counts for
13 papers · 1 filter
Learning Implicit Sentiment in Aspect-based Sentiment Analysis with Supervised Contrastive Pre-Training
Zhengyan Li, Yicheng Zou, Chong Zhang +2
Aspect-based sentiment analysis aims to identify the sentiment polarity of a specific aspect in product reviews. We notice that about 30% of reviews do not contain obvious opinion…
Plug-Tagger: A Pluggable Sequence Labeling Framework Using Language Models
Xin Zhou, Ruotian Ma, Tao Gui +3
Plug-and-play functionality allows deep learning models to adapt well to different tasks without requiring any parameters modified. Recently, prefix-tuning was shown to be a plug-a…
SAM: A Self-adaptive Attention Module for Context-Aware Recommendation System
Jiabin Liu, Zheng Wei, Zhengpin Li +4
Recently, textual information has been proved to play a positive role in recommendation systems. However, most of the existing methods only focus on representation learning of text…
Low-Resource Dialogue Summarization with Domain-Agnostic Multi-Source Pretraining
Yicheng Zou, Bolin Zhu, Xingwu Hu +2
With the rapid increase in the volume of dialogue data from daily life, there is a growing demand for dialogue summarization. Unfortunately, training a large summarization model is…
A Partition Filter Network for Joint Entity and Relation Extraction
Zhiheng Yan, Chong Zhang, Jinlan Fu +2
In joint entity and relation extraction, existing work either sequentially encode task-specific features, leading to an imbalance in inter-task feature interaction where features e…
Heterogeneous Graph Neural Networks for Keyphrase Generation
Jiacheng Ye, Ruijian Cai, Tao Gui +1
The encoder-decoder framework achieves state-of-the-art results in keyphrase generation (KG) tasks by predicting both present keyphrases that appear in the source document and abse…
Thinking Clearly, Talking Fast: Concept-Guided Non-Autoregressive Generation for Open-Domain Dialogue Systems
Yicheng Zou, Zhihua Liu, Xingwu Hu +1
Human dialogue contains evolving concepts, and speakers naturally associate multiple concepts to compose a response. However, current dialogue models with the seq2seq framework lac…
Template-free Prompt Tuning for Few-shot NER
Ruotian Ma, Xin Zhou, Tao Gui +4
Prompt-based methods have been successfully applied in sentence-level few-shot learning tasks, mostly owing to the sophisticated design of templates and label words. However, when…
SENT: Sentence-level Distant Relation Extraction via Negative Training
Ruotian Ma, Tao Gui, Linyang Li +3
Distant supervision for relation extraction provides uniform bag labels for each sentence inside the bag, while accurate sentence labels are important for downstream applications t…
One2Set: Generating Diverse Keyphrases as a Set
Jiacheng Ye, Tao Gui, Yichao Luo +2
Recently, the sequence-to-sequence models have made remarkable progress on the task of keyphrase generation (KG) by concatenating multiple keyphrases in a predefined order as a tar…
Who Responded to Whom: The Joint Effects of Latent Topics and Discourse in Conversation Structure
Lu Ji, Jing Li, Zhongyu Wei +2
Numerous online conversations are produced on a daily basis, resulting in a pressing need to conversation understanding. As a basis to structure a discussion, we identify the respo…
Larger-Context Tagging: When and Why Does It Work?
Jinlan Fu, Liangjing Feng, Qi Zhang +2
The development of neural networks and pretraining techniques has spawned many sentence-level tagging systems that achieved superior performance on typical benchmarks. However, a r…
Keyphrase Generation with Fine-Grained Evaluation-Guided Reinforcement Learning
Yichao Luo, Yige Xu, Jiacheng Ye +2
Aiming to generate a set of keyphrases, Keyphrase Generation (KG) is a classical task for capturing the central idea from a given document. Based on Seq2Seq models, the previous re…