Sister Help: Data Augmentation for Frame-Semantic Role Labeling (2021)

Abstract: While FrameNet is widely regarded as a rich resource of semantics in natural language processing, a major criticism concerns its lack of coverage and the relative paucity of its labeled data compared to other commonly used lexical resources such as PropBank and VerbNet. This paper reports on a pilot study to address these gaps. We propose a data augmentation approach, which uses existing frame-specific annotation to automatically annotate other lexical units of the same frame which are unannotated. Our rule-based approach defines the notion of a **sister lexical unit** and generates frame-specific augmented data for training. We present experiments on frame-semantic role labeling which demonstrate the importance of this data augmentation: we obtain a large improvement to prior results on frame identification and argument identification for FrameNet, utilizing both full-text and lexicographic annotations under FrameNet. Our findings on data augmentation highlight the value of automatic resource creation for improved models in frame-semantic parsing.

## introduction among the challenges to natural language processing (nlp) systems is access to sufficient and accurate information about the mapping between form and meaning in language @xcite . while the number of resources that provide such information has increased in recent decades, producing these resources remains labor-intensive and costly. this is particularly true for framenet (fn; @xcite , a rich resource of semantic annotations, popularized for semantic role labeling (srl) since the pioneering work of @xcite . nevertheless, framenet still relies (almost exclusively) on expert manual linguistic annotation, resulting in far fewer annotations than other resources that are able to scale up the annotation process easily @xcite . moreover, the car chuck bought a from for @xmath02,000 . jerry buyer goods seller money buy.v purchase.v ## method: data augmentation with sister lus the relationship among lus in a frame motivates examining their use for paraphrasing, where one lu replaces another resulting in an alternative phrasing of a sentence. @xcite produced paraphrases based on the relationship among lus of a frame or closely related frames, augmenting a meager data set for use in speech recognition. most importantly, lus in a frame tend overwhelmingly to follow the same annotation structure: the arguments of a lexical unit in a frame consist of the same fes, regardless of the lu. to this end, petruck and swayamdipta (2019) defined sister lexical unit as any lu for which annotation exists in a frame, and whose annotation can serve as a model to generate new annotation. a sister is the lu with annotation and an emptylu lacks annotation; the sister lu and the empty lu must be of the same part of speech. to generate augmented labeled data, we first identified all emptylus. for each such lu, we identified a corresponding sister, specifically, the lu with the greatest number of annotation sets and of the same pos as the emptylu in a given frame. we replaced each occurrence of a sister in an annotated sentence with the emptylu. the replacement process included steps to ensure that the newly generated instance included the correct word forms, i.e., singular and plural forms of nouns or conjugated verbs of a sister lu, following the strategy that rastogi and van durme (2014) employed, which augmented fn using a paraphrase database @xcite to add new lemmas and then rewrote existing annotated sentences with those lemmas. we transferred each sentence in the annotation set of the sister into that of each emptylu, and replaced instances of the sister with those of the emptylu. the replacement process ensured that newly generated data included correct word forms, i.e., singular and plural forms of nouns, conjugated verbs, or those with tense marking. ensuring agreement of tense, person, and number between instances of a sister and an emptylu required replacing the original word forms with those of the same grammatical number for nouns and tense, person, and number for verbs. however, this does not always yield correct results, e.g., for lemmas with irregular plural or past tense forms, as in ox, oxen and bring, brought. ## experiments to test the idea of leveraging frame-specific annotation to generate new annotation for lus lacking any, we wish to compare the performance of our approach with a baseline model. this facilitates determining the extent to which our augmentation algorithm would improve srl performance, and thus (potentially) contribute to the framenet corpus. our augmentation approach is applied to framenet version 1.7. ## conclusion and future work we outlined an approach to frame-srl that seeks to leverage existing annotation in a frame to generate new annotation for previously unannotated lus in the same frame, by exploiting the already identified target and frame of a given lemma. further, we demonstrated that augmentation of data improves frame-srl performance substantially. while we show the advantages of this approach on the open-sesame baseline, future work might involve studying if using this type of data augmentation will yield improvements on a state-of-the-art srl system, e.g. @xcite . several other interesting directions for future work exist, including the following: refining the augmentation algorithm to produce more grammatical sentences; streamlining open-sesame's code to run more efficiently; use active-learning to inform the data generation and to assist in the srl task; and experimentation with other computational techniques, as suggested by @xcite . these plans will advance defining processes to include frame-srl into framenet's development process, the ultimate goal of this work.