Refining Implicit Argument Annotation for UCCA (2020)

Abstract: Predicate-argument structure analysis is a central component in meaning representations of text. The fact that some arguments are not explicitly mentioned in a sentence gives rise to ambiguity in language understanding, and renders it difficult for machines to interpret text correctly. However, only few resources represent implicit roles for NLU, and existing studies in NLP only make coarse distinctions between categories of arguments omitted from linguistic form. This paper proposes a typology for fine-grained implicit argument annotation on top of Universal Conceptual Cognitive Annotation’s foundational layer. The proposed implicit argument categorisation is driven by theories of implicit role interpretation and consists of six types: Deictic, Generic, Genre-based, Type-identifiable, Non-specific, and Iterated-set. We exemplify our design by revisiting part of the UCCA EWT corpus, providing a new dataset annotated with the refinement layer, and making a comparative analysis with other schemes.

## introduction semantic representation frameworks have been a major medium to understanding the nature of languages for nlp. through these frameworks, researchers have been exploring linguistic phenomena such as quantification @xcite , coreference @xcite , and word sense @xcite . however, most efforts were put into studying linguistic complexity superficially, rather than the more latent, implicit omission of arguments in an event. for instance, in the sentence "just take the money!", the addressee who should "take the money" is left out. such omission cannot be recovered directly from the text in the way of gapping or ellipsis, but require a higher level of understanding and inference from the context. traditional studies approach argument omission from different aspects, namely syntactically, semantically, or pragmatically. the interpretation of implicit roles varies to a great extent, from phonological deleted role during production @xcite @xcite to timecourse reference omission from a psycholinguistic aspect @xcite . however, few studies have explored the implicit role phenomenon in nlp. in this paper, we propose a fine-grained cross-linguistically applicable implicit argument annotation typology as a refinement for universal conceptual cognitive annotation @xcite , ucca) categories. the typology follows ucca's design concept, focusing on the semantic notion of scene rather than linguistic form phenomena. the proposed implicit argument set contains six categories: deictic, generic, genre-based, type-identifiable, non-specific, and iterated-set. we refine the existing ucca relation labels and add information to them, while keeping all categories from the underlying annotation. our studies move ucca, a semantic representation framework, to have less syntactic definitions of implicit arguments. based on the proposed typology, we conduct a pilot annotation study, including revisit and refinement of the ucca ewt dataset, 1 and subsequently make a comparative analysis with the only other existing fine-grained implict role annotation scheme, fine-grained annotations of referential interpretation types @xcite . a few studies have explored the possibilities to parse implicit arguments. both gerber and chai (2012) and @xcite have developed parsers to recover implicit arguments for nominal predicates; @xcite parses against some linguistic constructions, two of which could license implicit arguments, that is, tough adjectives and verbal gerund; @xcite focus on resolving missing numeric fused-heads, which are implicit centers in ucca. providing the most up-to-date and fine-grained annotation of implicit arguments, our studies can potentially enhance natural language understanding if supplied with an adequate parser. for example, when companies conduct satisfaction analyses through web reviews, customers often express themselves colloquially in these reviews. examples include "serves bad ice cream, joe's is better" and "near a nice district, bad service and expensive." if these reviews are annotated with genre-based implicit arguments, referring to the conventional omission of reviewees, algorithms can study which part of the reviews really refers to the companies rather than other entities, and make better predictions, despite the omission of subjects and non-standard language. ## universal conceptual cognitive annotation furthermore, ucca distinguishes primary edges, appearing explicitly in one relation, from remote edges, allowing a scene to indicate its arguments by linking from another scene. primary edges form a tree while remote edges allow reentrancy, forming a dag. in some cases, an entity of importance in the interpretation of a scene does not explicitly exist in the text. hence, ucca introduces the notion of implicit units to represent such kind of entity. for instance, the sentence "have a real mechanic check before you leave" in figure 1 contains two scenes, evoked by "check" and "leave". the individual scenes are annotated as follows: in ucca's foundational layer, only limited cases of implicit arguments have been annotated. the main focus is on omission licensed by certain grammatical structures, foot_0 whose notion is similar to constructional null instantiation in framenet @xcite . two typical examples of such constructions are imperatives (forced omission of subjects) and passives (agent omission) in english: ## refined implicit corpus in furtherance of investigating the characteristics of ucca's implicit arguments, we piloted a study to revisit and refine part of english web treebank foot_1 annotated with the ucca foundational layer. foot_2 200 passages were randomly selected for experiment from the total 723 passages comprising the ucca ewt dataset. we use uccaapp to carry out annotation. the process is divided into two stages. firstly, we create passage-level review tasks to check the existing annotation whether they contain implicit arguments, and add missing arguments if necessary. secondly, we split passages into sentences, create tasks with refinement layer and then annotate with corresponding fine-grained implicit categories. since all the annotation works were undertaken by one single annotator, the dataset preferably serves as a demonstration of concept, and thus further measurement of inter-annotator agreement would be desired to establish a sounder dataset. ## conclusion and future work we proposed a novel typology for different implicit arguments in ucca, which allows annotation with a relatively low cognitive load. then we reviewed and refined part of the existing ucca english web treebank dataset and piloted annotation of our guidelines with a refinement layer of fine-grained implicit arguments. it is currently the only published dataset with this kind of information. our work addresses a deep linguistic problem in implicit role interpretation, impossible to deal with current machine learning approaches, and provides an example for conducting research about it. it has the potential to benefit natural language understanding, especially in genre-specific context, e.g., social media and customer reviews. in this work we only use the existing definitions from o'gorman (2019), testing their compatiblity with the ucca guidelines and their applicability to an ucca annotated corpus. in future work we would also like to reason about and possibly redesign the criteria based on semantic and pragmatic considerations @xcite . we will also consider the usefulness of the refined scheme for practical nlp tasks, and possibly discard some of the categories, such as iterated-set, which is the least frequent category in our corpus. it is anticipated that our study will inspire tailored design of implicit role annotation in other meaning representation frameworks. downstream tasks such as coreference resolution and human-robot interaction are also likely to benefit from reducing ambiguity and increase contextual understanding by explicitly modelling implicit arguments. while the pilot design is promising, existing parsers that tackle implicit arguments only attend to certain aspect of linguistic phenomena. therefore, for a rather comprehensive implicit argument annotation the like of ucca, it is crucial to develop a parser that can emit implicit nodes dynamically and label them with fair accuracy. finally, work on reviewing the corpus by a second annotator is underway to validate the annotation quality by providing an inter-annotator agreement measurement. future work will also expand the corpus and extend it to multiple languages.