Sarah Moeller, Ling Liu, Mans Hulden. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

An intermediate step in the linguistic analysis of an under-documented language is to find and organize inflected forms that are attested in natural speech.From this data, linguists generate unseen inflected word forms in order to test hypotheses about the language's inflectional patterns and to complete inflectional paradigm tables.To get the data linguists spend many hours manually creating interlinear glossed texts (IGTs).We introduce a new task that speeds this process and automatically generates new morphological resources for natural language processing systems: IGTto-paradigms (IGT2P).IGT2P generates entire morphological paradigms from IGT input.We show that existing morphological reinflection models can solve the task with 21% to 64% accuracy, depending on the language.We further find that (i) having a language expert spend only a few hours cleaning the noisy IGT data improves performance by as much as 21 percentage points, and (ii) POS tags, which are generally considered a necessary part of NLP morphological reinflection input, have no effect on the accuracy of the models considered here.

This paper presents a proposition bank for Russian (RuPB), a resource for semantic role labeling (SRL). The motivating goal for this resource is to automatically project semantic role labels from English to Russian. This paper describes frame creation strategies, coverage, and the process of sense disambiguation. It discusses language-specific issues that complicated the process of building the PropBank and how these challenges were exploited as language-internal guidance for consistency and coherence.

In the field of metaphor detection, deep learning systems are the ubiquitous and achieve strong performance on many tasks.However, due to the complicated procedures for manually identifying metaphors, the datasets available are relatively small and fraught with complications.We show that using syntactic features and lexical resources can automatically provide additional high-quality training data for metaphoric language, and this data can cover gaps and inconsistencies in metaphor annotation, improving state-of-the-art word-level metaphor identification.This novel application of automatically improving training data improves classification across numerous tasks, and reconfirms the necessity of high-quality data for deep learning frameworks.

Nathan Schneider, Jena D. Hwang, Vivek Srikumar, Jakob Prange, Austin Blodgett, Sarah R. Moeller, Aviram Stern, Adi Bitan, Omri Abend. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2018.