UA lberta at S em E val 2022 Task 2: Leveraging Glosses and Translations for Multilingual Idiomaticity Detection (2022)

Abstract: We describe the University of Alberta systems for the SemEval-2022 Task 2 on multilingual idiomaticity detection. Working under the assumption that idiomatic expressions are noncompositional, our first method integrates information on the meanings of the individual words of an expression into a binary classifier. Further hypothesizing that literal and idiomatic expressions translate differently, our second method translates an expression in context, and uses a lexical knowledge base to determine if the translation is literal. Our approaches are grounded in linguistic phenomena, and leverage existing sources of lexical knowledge. Our results offer support for both approaches, particularly the former.

## introduction in this paper, we describe the university of alberta systems for the task of classifying multi-word expressions (mwes) in context as either idiomatic or literal @xcite . each instance in the data includes a mwe (e.g., closed book), its language, and its context, composed of the three surrounding sentences. we participate in both the zero-shot and one-shot settings. while the exact definitions of the two key terms are not stated explicitly in the task description 1 , it is suggested that idiomatic is synonymous with non-compositional. the pocket oxford dictionary defines idiomatic as "not immediately comprehensible from the words used," and literal as "taking words in their basic sense." therefore, we adopt the following mwe compositionality criterion literal ≡ compositional ≡ ¬ idiomatic where the three terms are considered to be boolean variables. in addition, the shared task considers all proper noun mwes (e.g., eager beaver) as literal. our goal is to explore the idea that glosses and translations of word senses can help decide whether the meaning of a given mwe occurrence is compositional. based on the above-stated compositionality criterion, this in turn could facilitate idiomaticity detection. in particular, we hypothesize that at least one of the words in any idiomatic expression is used in a non-standard sense. following the intuition that a traditional word sense disambiguation (wsd) system can only identify senses that are included in a given sense inventory, we propose two methods that indirectly detect non-standard senses by leveraging either glosses or translations of senses from such an inventory. our gloss-based method follows from the intuition that the meaning of a given mwe occurrence is related to any of the existing sense glosses of its component words only if the expression is compositional. therefore, the addition of the glosses to the context of the expression should help the classifier in deciding whether the mwe is used in a literal or idiomatic sense. we implement this method by adding the glosses of each sense of each individual word, retrieved from a lexical knowledge base, to the input to a neural classifier which fine-tunes multilingual bert (mbert; @xcite for the idiomaticity detection task. we refer to this method as defbert (figure 1 ). our translation-based method follows from the observation that compositional expressions are typically translated word-for-word ("literally"), which implies that each content word and its translation should have the same meaning. therefore, each such multilingual word pair should share a multisynset in a multi-wordnet @xcite . the procedure is as follows: (1) translate the mwe in context; (2) word-align the source and target sentences; (3) lemmatize and pos-tag the source mwe; and (4) for each lemma in the mwe, search for a multi-synset that contains both the lemma and its translation. this method is unsupervised, and we refer to it as mt. our results provide evidence that leveraging lexical resources is beneficial for idiomaticity detection. in particular, our gloss-based method, when combined with a type-based unatt heuristic, is among the top-scoring submissions in the one-shot setting. the heuristic is based on the observation that some mwes are inherently idiomatic or literal, regardless of their context, which is confirmed by our analysis of the development set annotations. ## related work early attempts to represent idiomatic mwes involve treating idiomatic phrases as individual tokens and learning corresponding static embeddings @xcite . however, @xcite show that the effectiveness of this method is limited by data sparsity for longer idiomatic expressions. furthermore, @xcite and @xcite conclude that idiomaticity is not yet accurately represented even by contextual embedding models. tayyar madabushi et al. (2021) create a new manually labeled dataset containing idiomatic and literal mwes, and propose a method based on a pre-trained neural language model. regarding using lexical translations for idiomaticity detection, moirón and tiedemann (2006) measure semantic entropy in bitext alignment statistics, while @xcite predict compositionality by presenting an unsupervised method that uses wiktionary translation, synonyms, and definition information. we extend these ideas by applying machine translation, and consulting a multilingual lexical knowledge base. our prior work has already demonstrated the utility of lexical translations for various semantic tasks, including prior semeval tasks on predicting cross-lingual entailment @xcite and contextual synonymy detection @xcite , as well as word sense disambiguation @xcite , and homonymy detection @xcite . ## methods in this section, we describe our methods for idiomaticity detection. ## experiments we now describe our experiments, including the tools and resources, the experimental setup, the results, and a discussion of our findings. ## conclusion our top result ranks third overall in the one-shot setting. the corresponding method is applicable to a wide variety of languages. it takes advantage of the ability of neural language models to seamlessly incorporate textual information such as glosses, even if it is expressed in a different language. these results strongly support our hypothesis that the gloss information of individual words can improve idiomaticity detection. moreover, our development results support the hypothesis that non-compositional expressions can be identified through their translations. these findings conform with our prior work on leveraging translation for various semantic tasks (section 2). we hope that this work will motivate further investigation into the role of multilinguality in semantics.