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Shiman Zhao

5 accepted papers

2025

Instance Relation Learning Network with Label Knowledge Propagation for Few-shot Multi-label Intent Detection

IJCAI 2025

Few-shot Multi-label Intent Detection (MID) is crucial for dialogue systems, aiming to detect multiple intents of utterances in low-resource dialogue domains. Previous studies focus on a two-stage pipeline. They first learn representations of utterances with multiple labels and then use a threshold-

Cited by 0SourcePDFScholar
2025

Less is Enough: Relation Graph Guided Few-shot Learning for Multi-label Aspect Category Detection

ICASSP 2025accepted

Few-shot Multi-label Aspect Category Detection (FMACD) is an essential task, which aims to identify multiple aspect categories in a given sentence with limited data. Recently, the prototypical network as a mainline has been used for the task due to its powerful capacity. However, existing methods mo…

Cited by 0SourceScholar
2023

Learning Few-shot Sample-set Operations for Noisy Multi-label Aspect Category Detection

IJCAI 2023poster

Multi-label Aspect Category Detection (MACD) is essential for aspect-based sentiment analysis, which aims to identify multiple aspect categories in a given sentence. Few-shot MACD is critical due to the scarcity of labeled data. However, MACD is a high-noise task, and existing methods fail to addres…

Cited by 4SourcePDFScholar
2022

Learning Cooperative Interactions for Multi-Overlap Aspect Sentiment Triplet Extraction

EMNLP 2022finding

Aspect sentiment triplet extraction (ASTE) is an essential task, which aims to extract triplets(aspect, opinion, sentiment). However, overlapped triplets, especially multi-overlap triplets,make ASTE a challenge. Most existing methods suffer from multi-overlap triplets becausethey focus on the single…