Papers with collapsed models

2 papers
Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and Classification (P19-1)

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Challenge: Existing approaches to target sentiment analysis are limited by huge search space and sentiment inconsistency.
Approach: They propose a span-based extract-then-classify framework to detect opinion targets . they propose pipeline, joint, and collapsed models to classify polarities .
Outcome: The proposed framework outperforms the sequence tagging baseline on three benchmark datasets.
Dynamic Model-Bank Test-Time Adaptation for Automatic Speech Recognition (2025.emnlp-main)

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Challenge: Existing ASR TTA methods struggle with instability under continual and long-term distribution shifts.
Approach: They propose a continuous adaptive model-bank framework that adapts to domain shifts in ASR test-time scenarios.
Outcome: Experiments on diverse, continuously shifting ASR benchmarks show that DMSUTA outperforms existing continual TTA baselines.

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