Papers with manual annotation cost

3 papers
Fusion Makes Perfection: An Efficient Multi-Grained Matching Approach for Zero-Shot Relation Extraction (2024.naacl-short)

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Challenge: Existing methods to extract unseen relations require laborious manual annotation . a new approach uses fine-grained matching to reduce manual annotation cost .
Approach: They propose an efficient multi-grained matching approach that uses virtual entity matching to reduce manual annotation cost.
Outcome: The proposed approach outperforms the state-of-the-art methods and achieves inference efficiency and accuracy in zero-shot relation extraction tasks.
Multistage Fusion with Forget Gate for Multimodal Summarization in Open-Domain Videos (2020.emnlp-main)

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Challenge: Existing methods for multimodal summarization for open-domain videos lack fine-grained interactions between multisource inputs.
Approach: They propose a multistage fusion network with a forget gate module to integrate multimodal information into a fluent textual summary.
Outcome: The proposed model achieves state-of-the-art on multiple encoder-decoder architectures and low noise transcripts.
OpenForecast: A Large-Scale Open-Ended Event Forecasting Dataset (2025.coling-main)

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Challenge: Existing closed-ended event forecasting methods are constrained by a limited answer space.
Approach: They introduce OpenForecast, a large-scale open-ended dataset with three open-ending event forecasting tasks and an automatic LLM-based method for complex events.
Outcome: The proposed method can be used to evaluate the ability of complex event forecasting of large language models.

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