Papers with aggregation function
Weakly Supervised Attention Networks for Fine-Grained Opinion Mining and Public Health (D19-55)
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| Challenge: | Existing weakly supervised learning frameworks are used for segment classification . lack of segment labels prevents the use of standard supervised methods . |
| Approach: | They propose a model that uses weak supervision to train supervised models for segment-level classification . they propose sigmoid attention mechanism-based aggregation function to improve the model . |
| Outcome: | The proposed model outperforms state-of-the-art models for segment-level sentiment classification by 9.8% in F1 . |
MAD Speech: Measures of Acoustic Diversity of Speech (2025.naacl-long)
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| Challenge: | Recent advances in generative spoken language modeling have produced models that produce speech in a wide range of voices, prosody and recording conditions. |
| Approach: | They propose acoustic diversity metrics that measure voice, gender, emotion, accent, background noise and a priori known diversity preferences for each facet. |
| Outcome: | The proposed metrics show that they achieve stronger agreement with diversity than baselines. |
Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment (D18-1)
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| Challenge: | Existing approaches to network embeddings focus on one-hot representations of vertices, which are not able to capture relationships between verti- ces. |
| Approach: | They propose to integrate semantic features into network embeddings by matching important words between text sequences for all pairs of vertices. |
| Outcome: | The proposed framework outperforms state-of-the-art embedding methods on three real-world benchmarks for downstream tasks including link prediction and multi-label vertex classification. |