EAT: a simple and versatile semantic representation format for multi-purpose NLP

TL;DR

This paper introduces EAT, a simple, versatile semantic representation format for NLP that uses only three roles and positional encoding, facilitating various applications like text generation, corpus creation, and grammatical transformation.

Contribution

EAT is the first semantic format based on conjunctivist insights that simplifies roles and enhances versatility in NLP tasks.

Findings

EAT improves text generation quality from MRS representations.

EAT enables parallel corpus creation across grammatical classes.

EAT-based transformations effectively modify grammatical structures.

Abstract

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.