EAT：一种用于多用途NLP的简单通用语义表示格式
EAT: a simple and versatile semantic representation format for multi-purpose NLP
摘要：
语义表示是许多需要人类可解释数据的NLP任务的核心。连词主义框架-主要由Pietroski（2005，2018）开发-获得表达性表征，只有少数基本语义类型和与句法位置系统相关的关系。虽然表示简单性对于计算应用至关重要，但这些发现尚未对NLP产生重大影响。我们提出了第一个通用的语义表示格式的自然语言处理直接基于这些见解。我们命名的格式EAT由于其基础上的事件，代理，主题参数在新戴维森逻辑形式。它建立在这样一个想法上，即类似的三方论元关系在各个类别中普遍存在，并且可以在没有额外词汇信息的情况下从语法结构中构建。我们提出了一个详细的阐述EAT以及它如何与其他流行的格式在以前的工作中使用，如抽象意义表示（AMR）和最小递归语义（MRS）。EAT在两个方面脱颖而出：简单性和多功能性。独特的是，EAT放弃了语义元预测，而是完全通过位置编码来表示语义角色。这是通过将角色数量限制为只有三个而实现的;从AMR和MRS中认识到的几十个角色中大幅减少。EAT的简单性使其在应用中具有非常多的用途。首先，我们表明，在Hajdik et al.（2019）的测试环境中，基于EAT大幅减少语义角色有利于MRS的文本生成。其次，我们实现了EAT从句法分析的推导，并将其应用于语法类之间的并行语料库生成。第三，我们训练一个编码器-解码器LSTM网络来将EAT映射为英语。最后，我们使用编码器-解码器网络和基于规则的替代方案来进行语法转换EAT输入。
英文摘要：
Semantic representations are central in many NLP tasks that require human-interpretable data. The conjunctivist framework - primarily developed by Pietroski (2005, 2018) - obtains expressive representations with only a few basic semantic types and relations systematically linked to syntactic positions. While representational simplicity is crucial for computational applications, such findings have not yet had major influence on NLP. We present the first generic semantic representation format for NLP directly based on these insights. We name the format EAT due to its basis in the Event-, Agent-, and Theme arguments in Neo-Davidsonian logical forms. It builds on the idea that similar tripartite argument relations are ubiquitous across categories, and can be constructed from grammatical structure without additional lexical information. We present a detailed exposition of EAT and how it relates to other prevalent formats used in prior work, such as Abstract Meaning Representation (AMR) and Minimal Recursion Semantics (MRS). EAT stands out in two respects: simplicity and versatility. Uniquely, EAT discards semantic metapredicates, and instead represents semantic roles entirely via positional encoding. This is made possible by limiting the number of roles to only three; a major decrease from the many dozens recognized in e.g. AMR and MRS. EAT's simplicity makes it exceptionally versatile in application. First, we show that drastically reducing semantic roles based on EAT benefits text generation from MRS in the test settings of Hajdik et al. (2019). Second, we implement the derivation of EAT from a syntactic parse, and apply this for parallel corpus generation between grammatical classes. Third, we train an encoder-decoder LSTM network to map EAT to English. Finally, we use both the encoder-decoder network and a rule-based alternative to conduct grammatical transformation from EAT-input.
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