Scope-enhanced Compositional Semantic Parsing for DRT (2024)

Abstract: 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.

## introduction among current semantic representation formalisms used in nlp, discourse representation theory (drt; @xcite stands out in its systematic use of structural nesting and variable binding to represent meaning in detail. originating from linguistic theory, drt has been designed to capture subtle semantic and discourse phenomena such as anaphora, presupposition, and discourse structure, as well as tense and aspect (see fig. 1 ). this structural and semantic richness distinguishes drt from other popular frameworks in semantic parsing, such as abstract meaning representation (amr; @xcite . with the availability of the broad-coverage parallel meaning bank (pmb; @xcite , drt has become an active target for the development of semantic parsing methods. the current state of the art is held by purely neural seq2seq models @xcite . however, due to the structural complexity of typical drt representations, these models do not always generate well1 the code is accessible via https://github.com/ xiulinyang/compositional_drs_parsing . formed meaning representations. they also struggle on long sentences; length generalization is a known challenge for transformers in semantic parsing settings @xcite . existing compositional semantic parsers for drt significantly lag behind the seq2seq models in terms of parsing accuracy. in this paper, we introduce the ams parser, an accurate compositional drt parser. the ams parser extends the am parser @xcite , which predicts meaning representations compositionally and has achieved high accuracy across a range of sembanks @xcite . the am parser by itself struggles to predict structural nesting in drt. the key challenge is to predict scope: how to assign each atomic formula in fig. 1 (top) to one of the three boxes which represent scope at different levels. these boxes, which will be explained further in section 2.1, play a key role in organizing the logical relationships in drt. more specifically, in drt, these boxes, or scope, govern where discourse referents (like individuals or events) and logical operators (such as negation and quantifiers) are valid or accessible within the structure. this is essential for understanding the meaning of sen-tences, especially in nlp tasks that rely on correct interpretation of negation, quantification, and discourse relations. the technical contribution of this paper is to extend the am parser with an innovative mechanism for predicting scope. we train a dependency parser to predict scope relations between word tokens and project this information into the drt representation using word-to-box alignments. we show that this dependency mechanism can predict correct scope assignments at very high accuracy. the overall parser always predicts well-formed drt representations (in contrast to all seq2seq models) and is almost on par with the best models in parsing accuracy. on the pmb testlong split, which contains particularly long sentences, it outperforms all other drt parsers that are trained on the pmb gold dataset. thus, the strength of the ams parser is its ability to remain accurate as sentences grow complex. ## scope in drt is hard for the am parser we start with an attempt to directly apply the am parser to drt. as we will see, the dashed scope edges in a drg are difficult to handle with the am parser. we will solve this problem in the ams parser, presented in section 4. ## scope-enhanced am parsing we will address this scope challenge through a twostep process. first, we simplify the drgs by removing scope edges, such that over 94% of drgs can be decomposed for training. second, we recover the scope information at parsing time through an independent scope prediction mechanism. the child.n.01 misbehave.v.01 time.n.08 occasionally.r.01 agent time manner equ "now" n e a g t i o n negation negation figure 5: compact drg for fig 1 (the removed edges are marked in light blue). ## conclusion and future work in this work, we proposed a novel mechanism for predicting scope assignments in drt parsing. by combining it with the compositional am parser, we obtain the ams parser, which outperforms existing drt parsers trained on the same dataset, especially for complex sentences. we show that ams parser can parse the expressive meaning representation frameworks more easily than the seq2seq counterparts. it can be naturally applied to more expressive meaning representations and our scope resolver can be easily combined with all compositional parsers to parse non-compositional information. our parser also avoids the prediction of ill-formed drgs that plague other models. the prediction of scope information has been a long-standing challenge in computational semantics; our dependency parsing mechanism achieves very high accuracy on this task. in the future, we plan to extend our work to tackle increasingly complex meaning representation frameworks, such as uniform meaning representation (umr) @xcite . since umr-writer @xcite , the umr annotation tool, provides node-token alignment automatically, no more manual annotation is needed. furthermore, our current system's architecture, which includes both the am parser and a dependency parser by @xcite , presents opportunities for optimization. we aim to streamline the process by unifying these two models into a single framework that leverages joint learning.