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Encoder-decoder transformers advance constituent parsing accuracy

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员探索了使用预训练的编码器-解码器 Transformer 模型进行句法成分分析，这是自然语言理解的关键任务。他们的工作通过对 BART、mBART 和 T5 等模型进行微调以生成线性化解析树，扩展了现有的序列到序列方法。研究表明，与专用解析器相比，该方法取得了有竞争力的结果，并且在连续解析任务上超越了之前的序列到序列模型。 AI

影响 增强了句法解析能力，可能改进下游 NLP 应用。

排序理由 学术论文，详细介绍了现有模型在特定 NLP 任务中的新应用。

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

Encoder-decoder transformers advance constituent parsing accuracy

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报道来源 [2]

  1. arXiv cs.CL TIER_1 English(EN) · Cristina Outeiriño Cid ·

    利用预训练的编码器-解码器Transformer进行序列到序列的成分解析

    To achieve deep natural language understanding, syntactic constituent parsing plays a crucial role and is widely required by many artificial intelligence systems for processing both text and speech. A recent approach involves using standard sequence-to-sequence models to handle c…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    利用预训练的编码器-解码器 Transformer 进行序列到序列的成分句法分析

    To achieve deep natural language understanding, syntactic constituent parsing plays a crucial role and is widely required by many artificial intelligence systems for processing both text and speech. A recent approach involves using standard sequence-to-sequence models to handle c…

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