Manual Clustering and Spatial Arrangement of Verbs for Multilingual Evaluation and Typology Analysis

Proceedings of the 28th International Conference on Computational Linguistics, pages 4810–4824 Barcelona, Spain (Online), December 8-13, 2020 4810 Manual Clustering and Spatial Arrangement of Verbs for Multilingual Evaluation and Typology Analysis Olga Majewska, Ivan Vuli´c, Diana McCarthy, Anna Korhonen Language Technology Lab, Theoretical and Applied Linguistics, University of Cambridge {om304, iv250, alk23}@cam.ac.uk, diana@dianamccarthy.co.uk Abstract We present the first evaluation of the applicability of a spatial arrangement method (SpAM) to a typologically diverse language sample, and its potential to produce semantic evaluation resources to support multilingual NLP, with a focus on verb semantics. We demonstrate SpAM’s utility in allowing for quick bottom-up creation of large-scale evaluation datasets that balance cross-lingual alignment with language specificity. Starting from a shared sample of 825 English verbs, translated into Chinese, Japanese, Finnish, Polish, and Italian, we apply a two-phase annotation process which produces (i) semantic verb classes and (ii) fine-grained similarity scores for nearly 130 thousand verb pairs. We use the two types of verb data to (a) examine cross-lingual similarities and variation, and (b) evaluate the capacity of static and contextualised representation models to accurately reflect verb semantics, contrasting the performance of large language-specific pretraining models with their multilingual equivalent on semantic clustering and lexical similarity, across different domains of verb meaning. We release the data from both phases as a large-scale multilingual resource, comprising 85 verb classes and nearly 130k pairwise similarity scores, offering a wealth of possibilities for further evaluation and research on multilingual verb semantics. 1 Introduction Many recent efforts in semantic modeling have focused on unsupervised pretraining to extend the benefits offered by recently proposed text encoders (Devlin et al., 2019) to new languages and domains. In these approaches, general language representations are learned from large volumes of unlabeled text, and subsequently leveraged in downstream systems by means of fine-tuning on a given supervised task. The release of large multilingual pretrained encoders (Devlin et al., 2019; Conneau and Lample, 2019) boosted the state of the art on a range of multilingual tasks (Kondratyuk and Straka, 2019; Wang et al., 2019; Pires et al., 2019; Wu and Dredze, 2019; Hu et al., 2020; Artetxe et al., 2020; Qiu et al., 2020; Mueller et al., 2020). In parallel, the number of language-specific pretrained architectures available has also been steadily growing, with the advantage of being more attuned to the properties of the language in question (Virtanen et al., 2019; Nozza et al., 2020). The ease of incorporating these powerful encoders into downstream task pipelines has made them widely popular. However, there is a disproportionate shortage of resources allowing for probing of the learned representations in most languages. The aim of this work is to address this deficit by releasing a multilingual resource targeting verb semantics in a typologically diverse selection of languages where no such datasets have hitherto been available. The motivation behind the specific focus on verbs is twofold: (i) the importance of accurate and nuanced representation of verb meaning in light of their pivotal role in sentence structure and the …

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