Centrala begrepp
Introducing the Triple GNNs network to enhance DiaASQ by integrating syntactic and semantic information for improved quadruple extraction in dialogues.
Sammanfattning
The study introduces the Triple GNNs model to improve DiaASQ by combining intra-utterance syntactic details and inter-utterance semantic interactions. The model outperforms existing baselines on two datasets, showcasing its effectiveness. By focusing on both syntactic dependencies within utterances and interactions between utterances, the model enhances quadruple extraction in dialogues. The ablation study confirms the critical role of both components in the model's success. Overall, the Triple GNNs network significantly advances conversational aspect-based sentiment analysis.
Statistik
Experiments on two standard datasets reveal that our model significantly outperforms state-of-the-art baselines.
Our method achieves state-of-the-art performance.
The micro F1-score evaluates the entire quadruple, while identification-F1 focuses solely on the triple (t, a, o) and does not account for sentiment polarity.
Citat
"Our contributions can be summarized as follows: We introduce a novel Triple GNNs network to integrate intra-utterance syntactic information and inter-utterance semantic information."
"Our proposed method significantly outperforms the state-of-the-art on both the ZH and EN datasets."
"The elimination of the intra-GCN module leads to a marked decrease in overall performance."