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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 2 of 2 1 Keyphrase Generation with Fine-Grained Evaluation-Guided Reinforcement Learning ... Luo, Yichao; Xu, Yige; Ye, Jiacheng. - : arXiv, 2021 BASE Show details 2 Keyphrase Generation with Fine-Grained Evaluation-Guided Reinforcement Learning ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Xu, Yige. - : Underline Science Inc., 2021 Abstract: Aiming to generate a set of keyphrases, Keyphrase Generation (KG) is a classical task for capturing the central idea from a given document. Based on Seq2Seq models, the previous reinforcement learning framework on KG tasks utilizes the evaluation metrics to further improve the well-trained neural models. However, these KG evaluation metrics such as $F1@5$ and $F1@M$ are only aware of the exact correctness of predictions on phrase-level and ignore the semantic similarities between similar predictions and targets, which inhibits the model from learning deep linguistic patterns. In response to this problem, we propose a new fine-grained evaluation metric to improve the RL framework, which considers different granularities: token-level $F1$ score, edit distance, duplication, and prediction quantities. On the whole, the new framework includes two reward functions: the fine-grained evaluation score and the vanilla $F1$ score. This framework helps the model identifying some partial match phrases which can be ... URL: https://dx.doi.org/10.48448/dypg-4467 https://underline.io/lecture/38331-keyphrase-generation-with-fine-grained-evaluation-guided-reinforcement-learning BASE Hide details

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