面向DRT的辖域增强组合式语义解析
Scope-enhanced Compositional Semantic Parsing for DRT
AI总结：
针对现有seq2seq模型在DRT解析中准确率随句子复杂度下降、难生成合规表征的问题，提出基于新型量词辖域预测机制的组合式神经符号AMS解析器，可稳定输出合规结果，复杂句子解析表现更优。
摘要：
话语表征理论（Discourse Representation Theory，DRT）区别于其他语义表征框架的特点是，它能通过结构嵌套与变量约束对复杂语义及话语现象建模。尽管seq2seq模型在DRT解析任务上保持着当前最优水平，但其准确率会随句子复杂度提升而下降，有时还难以生成形式合规的DRT表征。我们提出AMS解析器——一款面向DRT的组合式神经符号语义解析器，它基于一种预测量词辖域的新机制构建。实验表明，AMS解析器能稳定生成形式合规的输出，在DRT解析任务上表现优异，尤其擅长处理复杂句子。
英文摘要：
Discourse Representation Theory (DRT) distinguishes itself from other semantic representation frameworks by its ability to model complex semantic and discourse phenomena through structural nesting and variable binding. While seq2seq models hold the state of the art on DRT parsing, their accuracy degrades with the complexity of the sentence, and they sometimes struggle to produce well-formed DRT representations. We introduce the AMS parser, a compositional, neurosymbolic semantic parser for DRT. It rests on a novel mechanism for predicting quantifier scope. We show that the AMS parser reliably produces well-formed outputs and performs well on DRT parsing, especially on complex sentences.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
