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pro vyhledávání: '"Pereira, Lis Kanashiro"'
Existing in-context learning (ICL) methods for relation extraction (RE) often prioritize language similarity over structural similarity, which can lead to overlooking entity relationships. To address this, we propose an AMR-enhanced retrieval-based I
Externí odkaz:
http://arxiv.org/abs/2406.10432
Autor:
Virgo, Felix, Cheng, Fei, Pereira, Lis Kanashiro, Asahara, Masayuki, Kobayashi, Ichiro, Kurohashi, Sadao
We propose a voting-driven semi-supervised approach to automatically acquire the typical duration of an event and use it as pseudo-labeled data. The human evaluation demonstrates that our pseudo labels exhibit surprisingly high accuracy and balanced
Externí odkaz:
http://arxiv.org/abs/2403.18504
Publikováno v:
SemEval 2022-Task 2
We propose a multilingual adversarial training model for determining whether a sentence contains an idiomatic expression. Given that a key challenge with this task is the limited size of annotated data, our model relies on pre-trained contextual repr
Externí odkaz:
http://arxiv.org/abs/2206.03025
We propose an ensemble model for predicting the lexical complexity of words and multiword expressions (MWEs). The model receives as input a sentence with a target word or MWEand outputs its complexity score. Given that a key challenge with this task
Externí odkaz:
http://arxiv.org/abs/2105.05535