Improving H ow N et-Based C hinese Word Sense Disambiguation with Translations (2022)

Abstract: Word sense disambiguation (WSD) is the task of identifying the intended sense of a word in context. While prior work on unsupervised WSD has leveraged lexical knowledge bases, such as WordNet and BabelNet, these resources have proven to be less effective for Chinese. Instead, the most widely used lexical knowledge base for Chinese is HowNet. Previous HowNet-based WSD methods have not exploited contextual translation information. In this paper, we present the first HowNet-based WSD system which combines monolingual contextual information from a pretrained neural language model with bilingual information obtained via machine translation and sense translation information from HowNet. The results of our evaluation experiment on a test set from prior work demonstrate that our new method achieves a new state of the art for unsupervised Chinese WSD.

## introduction word sense disambiguation (wsd) is the task of identifying the intended sense of a word in context. it has been identified as a central task in natural language understanding @xcite , and has been applied to downstream tasks such as machine translation @xcite ), text summarization @xcite , and sentiment classification @xcite . while supervised wsd systems consistently achieve the best performance, they require large sense-annotated training corpora, which are often difficult to obtain. to avoid this problem, unsupervised wsd systems instead rely on lexical knowledge bases, such as wordnet @xcite and babelnet @xcite . motivated by the relatively poor coverage of chinese by babelnet, a parallel line of research has adopted hownet @xcite as both a sense inventory and lexical knowledge base for chinese wsd. different from wordnet and babelnet, which define senses by synonymy and describe them using manually-crafted glosses, hownet associates each sense with a set of sememes, the minimal semantic units in natural language @xcite . in addition, hownet annotates each sense and sememe with an english translation, as shown in figure 1 . due to the differences between hownet and wordnet/babelnet, particularly their different sense inventories, most wsd systems designed for use with wordnet or a comparable resource cannot be applied to wsd datasets annotated with hownet senses. in this paper, we propose a new approach that incorporates both monolingual and multilingual information. for the former, we apply a neural language model pretrained for the masked word prediction task to generate a list of contextuallyappropriate substitutions for the target word. for the latter, we exploit the observation that different senses of a word may translate differently @xcite , and leverage a machine translation model to obtain english word translations. our experiments show that the proposed method sets a new state of the art for this task on a dataset used in prior work. we observe an improvement of 2.6% micro-f1 score on the test set, including an improvement of nearly 4% on verbs. we also perform a comparative study which suggests that combining heterogeneous translation methods yields further improvements. our contributions are as follows: (1) a novel method for unsupervised chinese word sense disambiguation with the hownet sense inventory, which is the first to explicitly leverage translations. ## prior work hownet is one of the most widely used lexical knowledge bases in chinese nlp research. while babelnet is a popular resource for multilingual tasks, its coverage of chinese is relatively poor; indeed, during our experiments, many words covered by hownet could not be found in babelnet. moreover, we found issues with the accuracy of the chinese information that babelnet does include. in contrast, hownet provides much more complete coverage with more than 100,000 chinese words, each with its own inventory of manually defined senses. in its 20-year history, it has become very well-known in the chinese nlp community, with frequent updates and a python api. we have noticed increasing usage of hownet in recent years for tasks such as sequence modeling @xcite , sememe prediction @xcite ), text matching @xcite , adversarial attacks @xcite , sense embedding @xcite and language representation @xcite . most hownet-based wsd systems are unsupervised @xcite and semi-supervised @xcite , due to the lack of large corpora annotated with hownet senses. @xcite perform sense selection based on co-occurrence statistics between the target words and the words in its context. @xcite produce a sense embedding by averaging the embeddings of its sememes, and then compute the cosine similarity between each sense embedding and an embedding of the context of the target word. the method of @xcite is designed to use wordnet synset information to create sense embeddings and compute similarity scores between each sense and the context. they adapt this method for use with hownet by using sememe information to approximate synsets. the current state-of-the-art method of @xcite uses substitution word scores from a masked language model to choose the sense that best fits in the context. we are not aware of prior methods which leverage translation information from hownet; however, @xcite exploit translation knowledge from babelnet. our hownet-based wsd task is more challenging, since, unlike babelnet, hownet does not include synonyms for each sense, and translations are only available in english, precluding the synergistic use of multiple languages. ## methods the goal of our task is to map a given word x t (the "target" word) in a given context to the correct sense, as represented in a given sense inventory. thus, each wsd instance is essentially a multiclass classification problem, with the sense inventory comprising the set of classes. formally, given a source text @xmath0, . . . , x t , . . . , x t ) as input, we try to identify the most suitable sense s i t among the n xt hownet senses for x t . we assume that the chinese input text is tokenized, which is the case in the datasets that we work with. our approach uses a carefully designed scoring function which combines monolingual contextual information from a pretrained neural language model with bilingual translation information from a machine translation model. in this section, we describe how the language model, hownet sense representation, and translation knowledge are combined. an overview of our approach is shown in figure 2 . ## experiments in this section, we empirically evaluate our method, and compare its results to methods from prior work. we also include a comparison of various translation models. ## conclusion we have demonstrated that translation information can improve chinese word sense disambiguation using hownet. our application of both monolingual and multilingual information requires translations in only a single language, and does not require information about synonymy or other semantic relations, which are required by many unsupervised wsd methods. our methods set a new state-of-theart for unsupervised wsd in chinese. in the future, we plan to further investigate combining multiple machine translation systems to further improve our results.