Papers with word-level attention
Understanding Pure Character-Based Neural Machine Translation: The Case of Translating Finnish into English (2020.coling-main)
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| Challenge: | Recent work shows that deeper character-based neural machine translation models outperform subword-based models. |
| Approach: | They propose to investigate the ability of character-based models to learn word senses and morphological inflections and the attention mechanism in Finnish into English translation. |
| Outcome: | The character-based models outperform subword-based model in Finnish to English translation. |
Hierarchical Modeling for User Personality Prediction: The Role of Message-Level Attention (2020.acl-main)
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| Challenge: | Language processing is increasingly finding use as a supplement for questionnaires to assess psychological attributes of consenting individuals, but most approaches neglect to consider whether all documents of an individual are equally informative. |
| Approach: | They propose a model that uses message-level attention to learn the relative weight of users’ social media posts for assessing their five factor personality traits. |
| Outcome: | The proposed model outperforms models with word-level attention and yields state-of-the-art accuracies for all five personality traits. |
Reinforcement Learning–Guided Adaptive Tuning for Out-of-Distribution Harmful Text Detection (2026.acl-long)
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| Challenge: | Existing methods for testing harmful information on social media rely on fixed parameters that fail to handle substantial semantic discrepancies . RLAT can be used to adapt to semantic variations while preventing overfitting from continuous tuning. |
| Approach: | They propose a reinforcement learning-guided adaptive tuning method for harmful text detection that optimizes consistency loss and applies word-level attention constraints to reduce over-reliance on local words. |
| Outcome: | The proposed method outperforms state-of-the-art models in cross-platform and cross-temporal scenarios across multiple public datasets. |