Semantic Role Labeling Meets Definition Modeling: Using Natural Language to Describe Predicate-Argument Structures (2022)

Abstract: One of the common traits of past and present approaches for Semantic Role Labeling (SRL) is that they rely upon discrete labels drawn from a predefined linguistic inventory to classify predicate senses and their arguments.However, we argue this need not be the case. In this paper, we present an approach that leverages Definition Modeling to introduce a generalized formulation of SRL as the task of describing predicate-argument structures using natural language definitions instead of discrete labels. Our novel formulation takes a first step towards placing interpretability and flexibility foremost, and yet our experiments and analyses on PropBank-style and FrameNet-style, dependency-based and span-based SRL also demonstrate that a flexible model with an interpretable output does not necessarily come at the expense of performance. We release our software for research purposes at https://github.com/SapienzaNLP/dsrl.

## introduction commonly regarded as one of the key ingredients for natural language understanding @xcite , semantic role labeling @xcite aims at identifying "who did what to whom, where, when, and how?" within a given sentence @xcite . more precisely, for each predicate in the sentence, the task requires: i) selecting its most appropriate sense from a predetermined linguistic inventory; ii) identifying its arguments, i.e., those parts of the sentence that are semantically related to the predicate; and, iii) assigning a semantic role to each predicate-argument pair, as shown in figure 1 . due to the potential uses of these semantically rich structures, the research community has seen steady progress in the task, and srl has been shown to be beneficial mary gave the book to john transfer possession agent, giver entity given recipient, given to ???.0x mary googled the book for john search for information agent, searcher entity searched searched for a b figure 1 : a: srl annotations using predicate sense and semantic role labels (top) compared with their natural language definitions (bottom). b: the semantics of sense and role labels is undefined for out-of-inventory predicates (e.g., the inventories used for @xcite do not include an entry for "google"), but we can still use valid natural language definitions. for an increasingly wide range of applications in natural language processing (nlp), such as question answering @xcite , information extraction @xcite , machine translation @xcite , and summarization @xcite , as well as in computer vision for situation recognition @xcite and video understanding @xcite , inter alia. an important yet often overlooked aspect of srl is that, since its conception, the formulation of the task has generally relied upon predetermined linguistic resources, such as framenet @xcite , propbank @xcite , verb-net @xcite and, more recently, verbatlas @xcite , which provide the labels to be used for tagging predicates and their arguments with senses and semantic roles, respectively. therefore, to this day, srl has been framed predominantly as a classification task in which systems assign discrete labels to portions of a sentence (figure 1a , top). although recent systems have achieved impressive results on standard benchmarks @xcite in english @xcite as well as in multilingual srl @xcite , we observe and emphasize that relying upon discrete labels raises the following critical questions: we hope that our semantically-driven descriptions in natural language, free of resource-specific labels that require expert knowledge of srl, will not only enable easier integration of sentence-level semantics into downstream applications but also provide valuable insights to nlp researchers. recent approaches in srl. independently of the linguistic inventory of choice, given the complexity of the task, early work often employed separate systems for each step of the srl pipeline @xcite . however, in recent years, researchers have successfully managed to develop end-to-end approaches @xcite , especially due to the increasing expressiveness of recent neural architectures. since then, the attention of the community has mainly focused on when syntactic features are useful @xcite or can be dispensed with @xcite . further to this, several studies have also investigated the effectiveness of their proposed approaches on different annotation formalisms, namely, dependency-and after, for simplicity, we follow propbank and call them senses and semantic roles, respectively, independently of the resource. definition modeling. the task of definition modeling was originally concerned with producing a natural language definition for a given word and its corresponding embedding @xcite . the formulation of the task was later generalized to take polysemy into account, as the same word may convey different meanings depending on the context it appears in. although introduced a few years ago now, definition modeling has attracted significant interest @xcite and has found success in semantic tasks @xcite such as word sense disambiguation @xcite and word-in-context (pilehvar and camacho-collados, 2019, wic). motivated by the success of definition modeling, we propose a novel generalization of its formulation, in which the objective is to use natural language not only to define a target word in context but also to describe its semantically-relevant sentential constituents. ## describing predicate-argument structures using natural language in this section, we introduce our novel reformulation of the srl task (section 3.1), describe dsrl, a simple yet effective autoregressive approach for it (section 3.2), and show how to use dsrl to perform standard srl (section 3.3). ## conclusion recent progress in srl has mainly revolved around the development of state-of-the-art systems which, however, are bound to specific predicateargument inventories. in this paper, instead, we proposed a novel task formulation that takes a step towards putting interpretability and flexibility in the foreground: we reframed srl as the task of describing the predicate-argument structure of a sentence using natural language only, which is human-interpretable by definition. our experiments, supported by in-depth analyses, demonstrated that prioritizing interpretability does not come at the expense of performance. furthermore, our approach is flexible enough to achieve competitive or even state-of-the-art results on popular gold standard benchmarks for srl, showing that natural language can act as a bridge between heterogeneous linguistic resources, e.g., propbank and framenet, and also annotation formalisms, e.g., dependency-or span-based srl. we hope that our model will foster research in high-performance yet interpretable systems in nlp, and provide a means towards achieving easier integration of sentencelevel semantics into downstream applications.