Improving Word Sense Disambiguation with Translations (2020)

Abstract: It has been conjectured that multilingual information can help monolingual word sense disambiguation (WSD). However, existing WSD systems rarely consider multilingual information, and no effective method has been proposed for improving WSD by generating translations. In this paper, we present a novel approach that improves the performance of a base WSD system using machine translation. Since our approach is language independent, we perform WSD experiments on several languages. The results demonstrate that our methods can consistently improve the performance of WSD systems, and obtain state-ofthe-art results in both English and multilingual WSD. To facilitate the use of lexical translation information, we also propose BABALIGN, an precise bitext alignment algorithm which is guided by multilingual lexical correspondences from BabelNet.

## introduction word sense disambiguation (wsd) is one of the core tasks in natural language processing. given a predefined sense inventory, a wsd system aims to identify the correct sense of a content word in context. although wsd is a monolingual task, it has been conjectured that multilingual information could help @xcite . attempts have been made to leverage parallel corpora for sense tagging @xcite , but no effective method for improving wsd with translations has been proposed to date. much of the history of wsd has been determined by the availability of manually created lexical resources in english, including semcor @xcite and wordnet @xcite . the situation changed with the introduction of babel-net @xcite , a massive multilingual semantic network, created by automatically integrating wordnet, wikipedia, and other resources. in particular, babelnet synsets contain translations in multiple languages for each individual word sense. methods have been proposed to use multilingual information in babelnet for wsd @xcite , but they do not directly exploit the mapping between senses and translations in multiple languages. while there have been many attempts to apply wsd to machine translation (mt) @xcite , our goal instead is to harness advances in mt to improve wsd. rather than develop a new wsd system, we propose a general method that can make existing and future systems more accurate by leveraging translations. we evaluate our methods with several supervised and knowledge-based wsd systems. our approach is based on the assumption of absolute synonymy between the senses of mutual translations in context @xcite . the principal method softconstraint refines sense predictions of a given base wsd system using sense-translation mappings from babelnet. the approach is able to take advantage of translations in multiple languages, whether produced manually or by mt models. it is also able to leverage sense frequency information, which can be obtained in either a supervised or unsupervised manner. another method that we test is t emb which integrates translations as contextual word embeddings into a wsd system to bias its sense predictions. to obtain word-level translations from the translated contexts, we introduce babalign, a precise alignment algorithm guided by babelnet synsets. in figure 1 , we show the entire architecture of our model based on aforementioned components. our experimental results demonstrate that translations can significantly improve existing wsd systems. we perform several experiments on english and multilingual wsd with both manual and machine translations. in the english wsd experiments with manual translations and word-level alignments, we determine the potential of our methods in an ideal situation. in the experiments with machine translations, we validate that the methods are effective and robust by showing improvements over existing wsd systems. finally, in the multilingual wsd experiments, we demonstrate the language independence of our methods. the main contributions of this work are the following. (1) we propose the first effective method to improve wsd with automatically generated translations. (2) our language-independent knowledgebased method achieves state-of-the-art results in both english all-words and multilingual wsd. ## related work the integration of multilingual information to improve english wsd has been considered in prior work. through analyzing a multilingual dictionary, @xcite observe that highly distinct senses can translate differently. @xcite propose a wsd system based on translation information extracted from a bitext, but it fails to outperform systems that rely on monolingual information only. word sense induction (wsi) and cross-lingual wsd (clwsd) are related tasks. wsi aims for automatically inducing word senses from corpora by clustering similar instances of words. several prior works perform wsi based on bitexts to create bilingual sense inventory on word samples, where translations are treated as sense tags @xcite . clwsd is a task to predict a set of translations for a given ambiguous word in context. attempts have been made to integrate translations as bag-of-words feature vectors to enhance clwsd @xcite . since the goals of wsi and clwsd differ from standard wsd with predefined senses, our approach is not directly comparable. navigli and ponzetto (2012b) incorporate translations in babelnet synsets as a feature in a graphbased wsd system. however, rather than apply translations of the focus word token as constraints, they simply consider all possible translations of the focus word type to enhance its sense distinctions. apidianaki and gong (2015) directly apply sense-translation mappings in babelnet as a hard constraint on sense predictions using translations from sense-annotated bitexts. unlike our work, their approach is based on the babelnet first sense (bfs) baseline, rather than on an actual wsd system. their results on english wsd fail to show improvement over the baseline, which may be due to the use of only a single target language, as well as word alignment errors. ## methods we first formulate our wsd task. the input is a sentence, in which one word, e, is designated as the focus word. the set of possible senses of the focus word s(e) comes from the sense inventory. we assume that a base wsd system assigns a probability or score to each sense, with the output being the sense with the maximum score. the objective is to determine which sense @xmath0. we propose two methods, hardconstraint and softconstraint, which can be used to augment any base wsd system that meets the above specifications. both methods leverage translations in order to constrain sense predictions made by a base wsd system. in addition, we introduce a method of leveraging contextual word embeddings to enhance the integration of translations in combination with those constraints. finally, since our methods crucially depend upon identifying the translation of the focus word in the translated sentence, we also introduce a new knowledge-based word alignment algorithm. ## word alignment evaluation to show the effectiveness of babalign, which combines an existing word aligner with translations from babelnet, we evaluate the alignment performance using parallel datasets with gold alignment. we employ fastalign as the base aligner. as algorithm 1 babalign input: list of all source tokens, σ @xmath1, . . . , w sl ) list of all target tokens, σ @xmath2, . . . , w tm ) babelnet translations, bn(w s ) = {l 1 , . . . , l n } 1: a ← basealigner(σ s , σ t ) 2: for each aligned word pair (w s , w t ) ∈ a do 3: if w @xmath3: c ← compound search(w s , w t ) 5: modify a such that w s aligns to c. 6: else 7: l ← bnlex search(w s ) 8: modify a such that w s aligns to l. ## wsd evaluation in this section, we first describe the wsd systems that we use in our experiments. we then show how our methods can improve existing wsd systems in the oracle setting for english all-words wsd. finally, we report the results of the experiments on multilingual wsd and english all-words wsd with automatic translations. ## conclusion we proposed a novel approach that improves wsd by leveraging translations from multiple languages, which incorporates a knowledge-based bitext alignment. we tested our methods with several base wsd systems. we demonstrated experimentally that softconstraint can consistently improve wsd performance even when no manual translations are available, leading to state-of-the-art results on multilingual and english all-words wsd. we make the source code available at https:// github.com/yixingluan/translations4wsd.