activity
20192026
most citedEarlier Attention? Aspect-Aware LSTM for Aspect-Based Sentiment Analysis

7 citations · 15 across the 7 of their papers we have counts for

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Showing cs.CLShow all

9 papers · 1 filter

cs.CL2024

HCL: Hybrid and Cooperative Contrastive Learning for Cross-lingual Spoken Language Understanding

Bowen Xing, Ivor W. Tsang

State-of-the-art model for zero-shot cross-lingual spoken language understanding performs cross-lingual unsupervised contrastive learning to achieve the label-agnostic semantic ali…

cs.CL2023

Exploiting Contextual Target Attributes for Target Sentiment Classification

Bowen Xing, Ivor W. Tsang

Existing PTLM-based models for TSC can be categorized into two groups: 1) fine-tuning-based models that adopt PTLM as the context encoder; 2) prompting-based models that transfer t…

cs.CL2023

Co-guiding for Multi-intent Spoken Language Understanding

Bowen Xing, Ivor W. Tsang

Recent graph-based models for multi-intent SLU have obtained promising results through modeling the guidance from the prediction of intents to the decoding of slot filling. However…

cs.CL20231 cited

Relational Temporal Graph Reasoning for Dual-task Dialogue Language Understanding

Bowen Xing, Ivor W. Tsang

Dual-task dialog language understanding aims to tackle two correlative dialog language understanding tasks simultaneously via leveraging their inherent correlations. In this paper,…

cs.CL20225 cited

Co-guiding Net: Achieving Mutual Guidances between Multiple Intent Detection and Slot Filling via Heterogeneous Semantics-Label Graphs

Bowen Xing, Ivor W. Tsang

Recent graph-based models for joint multiple intent detection and slot filling have obtained promising results through modeling the guidance from the prediction of intents to the d…

cs.CL20222 cited

Group is better than individual: Exploiting Label Topologies and Label Relations for Joint Multiple Intent Detection and Slot Filling

Bowen Xing, Ivor W. Tsang

Recent joint multiple intent detection and slot filling models employ label embeddings to achieve the semantics-label interactions. However, they treat all labels and label embeddi…