Papers with pre-trained embedding

2 papers
Employing Glyphic Information for Chinese Event Extraction with Vision-Language Model (2024.findings-emnlp)

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Challenge: Recent studies on event extraction have incorporated a variety of features, including textual elements and annotations.
Approach: They propose a glyphic multi-modal Chinese event extraction model with hieroglyphic images to capture morphological structure from the sequence.
Outcome: The proposed model can extract events from a Chinese and KBP Eval datasets at low cost.
Few-Shot Representation Learning for Out-Of-Vocabulary Words (P19-1)

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Challenge: Existing methods for learning word embedding assume there are enough occurrences for each word in the corpus to accurately estimate the representation of words.
Approach: They propose to fit a representation function to predict an oracle embedding vector based on limited contexts.
Outcome: The proposed model outperforms existing methods in constructing an accurate embedding for OOV words and improves downstream tasks when the embeddable is utilized.

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