Papers with classification-based models
Large Scale Author Obfuscation Using Siamese Variational Auto-Encoder: The SiamAO System (2020.starsem-1)
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| Challenge: | Existing approaches to author obfuscation are largely heuristic, but they can be used to attack author identification. |
| Approach: | They propose a deep learning architecture for constructing adversarial examples against similarity-based learners and explore its application to author obfuscation. |
| Outcome: | The proposed architectures show that they can be used to attack author obfuscation . the proposed architecture shows that it can be applied to obliquacy of text . |
Cluster-Guided Label Generation in Extreme Multi-Label Classification (2023.eacl-main)
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| Challenge: | Existing classification-based models are poorly per-form for tail labels and ignore semantic relations among labels. |
| Approach: | They propose to guide label generation using label cluster information to hierarchically generate lower-level labels. |
| Outcome: | The proposed model outperforms classification and generation baselines on tail labels and improves in four popular XMC benchmarks. |
Where are you from? Geolocating Speech and Applications to Language Identification (2024.naacl-long)
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Patrick Foley, Matthew Wiesner, Bismarck Odoom, Leibny Paola Garcia Perera, Kenton Murray, Philipp Koehn
| Challenge: | Language identification (LID) is a critical component in many modern multilingual speech technologies. |
| Approach: | They propose to use radio broadcasts with known origin to train regression models . they also propose to explore using geolocation as a proxy task for LID . |
| Outcome: | The proposed model outperforms pretrained models on the FLEURS benchmark and on the VoxLingua benchmark. |
AMPERE: AMR-Aware Prefix for Generation-Based Event Argument Extraction Model (2023.acl-long)
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| Challenge: | Existing generation-based EAE models focus on problem re-formulation and prompt design without incorporating additional information that has been shown to be effective for classification-based models. |
| Approach: | They propose to incorporate AMR into generation-based EAE models by generating AMR-aware prefixes for every layer of the generation model. |
| Outcome: | The proposed model generates AMR-aware prefixes for every layer of the generation model and improves the generation. |
Hierarchical Selection of Important Context for Generative Event Causality Identification with Optimal Transports (2024.lrec-main)
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| Challenge: | Existing methods for Event Causality Identification (ECI) rely on external toolkits or human annotation to obtain training signals. |
| Approach: | They propose a generative framework that leverages Optimal Transport to automatically select the most important sentences and words from full documents. |
| Outcome: | The proposed framework can predict causal relation between two events in text without external tools. |