Multilingual Word Sense Disambiguation with Unified Sense Representation (2022)

Abstract: As a key natural language processing (NLP) task, word sense disambiguation (WSD) evaluates how well NLP models can understand the fine-grained semantics of words under specific contexts. Benefited from the large-scale annotation, current WSD systems have achieved impressive performances in English by combining supervised learning with lexical knowledge. However, such success is hard to be replicated in other languages, where we only have very limited annotations. In this paper, based on that the multilingual lexicon BabelNet describing the same set of concepts across languages, we propose to build knowledge and supervised based Multilingual Word Sense Disambiguation (MWSD) systems. We build unified sense representations for multiple languages and address the annotation scarcity problem for MWSD by transferring annotations from rich sourced languages. With the unified sense representations, annotations from multiple languages can be jointly trained to benefit the MWSD tasks. Evaluations of SemEval-13 and SemEval-15 datasets demonstrate the effectiveness of our methodology.

## introduction as a critical natural language understanding task, word sense disambiguation (wsd) aims at classifying words into pre-defined senses. with such a disambiguation process, machines can understand the precise meanings of words. previous researches have demonstrated that a sound wsd system could benefit many downstream nlp tasks, such as machine translation @xcite and information extraction @xcite . existing researches on word sense disambiguation mostly focus on english only. by leveraging lexical knowledge such as gloss @xcite @xcite or graph structure wsd example. language: english context: detailed studies of the plan were well underway. language: french(fr) inventory: lemma: plan pos_tag: noun sense keys: [bn:00062759n, bn:00005439n, bn:00062766n,…] sense key source gloss (sense definitions) bn:00062759n wordnet a series of steps to be carried out or goals to be accomplished bn:00062759n wikipedia outline of a strategy for achievement of an objective bn:00026536n wordnet an arrangement scheme … language: english(en) inventory: lemma: plan pos_tag: noun sense keys: [bn:00062759n, bn:00005439n, bn:00026536n,…] babelnet shown in figure 2 . the knowledge can be injected into supervised mwsd systems. second, the annotations acquired from rich sourced languages through machine translation and alignment tools, can be used as weak supervision. by utilizing the lexical knowledge and weak annotations, we can build a decent mwsd system for scarce sourced languages without further human effort. to summarize, the contributions of this paper are two-fold: (1) we propose to build an mwsd system mbert-uni for multiple languages with transferred annotations from rich sourced languages and unified synsets with lexical knowledge, addressing the data paucity problem on the mwsd task; (2) our system can be easily combined with other data generation efforts such as mulan @xcite , further boosting the system performance. experiments results on benchmark semeval-13 @xcite and semeval-15 @xcite demonstrate the effectiveness of our methodology. our code is open-resourced 1 . ## related work this section introduces previous efforts on multilingual word sense disambiguation, which can be categorized into two streams: data-driven systems and knowledge-based systems. ## approach in this section, we first present the formal definition of the multilingual wsd task and used notations. after that, we present the details of the proposed system mbert-uni, a supervised framework incorporating lexical unified representation space for the mwsd task. from the overview in figure 3 , we can see that mbert ## experiment setup in this section, we introduce the experiment details including the evaluation dataset, the evaluation metric, training corpora, baseline methods, and implementation details. ## result analysis in this section, we analyze the performances of our proposed mbert-uni model in two parts. we first introduce the effects of mbert-uni on mwsd task with our generated translated corpora. after that, we present further experiment results on the mwsd task under various settings. ## conclusion and future work in this paper, to build feasible knowledge and supervised based systems for multilingual word sense disambiguation, we propose to construct unified sense representation by utilizing babel synsets, and transferred annotations from rich source languages by machine translation and alignment tools. with the unified representations, previous data generation efforts can be combined and further boost the performance. moreover, annotations from different languages can be jointly trained and benefit the multilingual word sense disambiguation task. experiments on standard evaluation multilingual word sense disambiguation benchmarks demonstrate the effectiveness of the proposed method. future work can be extended on how to induce more lexical knowledge from various languages to improve the representation learning. moreover, based on the fact that multiple languages share a set of concepts described by babel synsets, the generated representations may benefit cross lingual representation for other natural language understanding tasks.