Automating Interlingual Homograph Recognition with Parallel Sentences (2022)

Abstract: Interlingual homographs are words that spell the same but possess different meanings across languages. Recognizing interlingual homographs from form-identical words generally needs linguistic knowledge and massive annotation work. In this paper, we propose an automatic interlingual homograph recognition method based on the cross-lingual word embedding similarity and co-occurrence of form-identical words in parallel sentences. We conduct experiments with various off-the-shelf language models coordinating with cross-lingual alignment operations and co-occurrence metrics on the Chinese-Japanese and English-Dutch language pairs. Experimental results demonstrate that our proposed method is able to make accurate and consistent predictions across languages.

## introduction when learning a foreign language, we often come across words in different languages sharing identical spellings. this is commonly seen in languages with similar writing systems. such formidentical words with the same or very similar semantic meaning across languages are called cognates. however, there may also be words that are identical in spelling but different in meanings, these words are called interlingual homographs. 1 for instance, the dutch word "angel" means "insect's sting", as opposed to its form-identical word in english. it is not unique for phonographic writing systems. in languages sharing logographic writing systems @xcite such as chinese and japanese, we can also see interlingual homograph examples like the word "平和", which means "gentle" in chinese, whereas "peace" in japanese. in this study, we propose a method for interlingual homograph recognition that is applicable if parallel sentences are available. we calculate similarity scores to measure the semantic similarities of form-identical word pairs, based on which we identify whether each form-identical word pair is cognate or homograph. as we aim to require no linguistic knowledge, our proposed method does not rely on bilingual dictionaries, and all tools, including embedding models and parallel sentences, can theoretically be obtained from raw corpus such as wikipedia. to verify the effectiveness of the proposed method, we conduct experiments on two pairs of languages that are etymologically distant from each other, namely, chinese-japanese and english-dutch. experimental results demonstrate that our proposed method can achieve accurate and consistent predictions across languages without depending on relevant linguistic knowledge and massive annotation work. ## methodology we tackle the interlingual homograph recognition. since form-identical word pairs do not differ in appearance, recognition must be based on clues other than their appearance. we thus formulate our criterion with the following two components: word embedding similarity and degree of cooccurrence in parallel sentences. the former is based on the simple intuition that if an interlingual form-identical word pair is interlingual homograph, the embeddings should not be similar in the crosslingual word embedding space. the latter is based on the intuition that if an interlingual form-identical word pair is cognate, it is likely to co-occur in a parallel sentence, whereas if it is interlingual homograph, it should be less likely. figure 1 illustrates the overview of our proposed method. given a pair of form-identical words, we get a similarity score by computing the cosine similarity of embeddings across languages. we also extract degree of co-occurrence from parallel sentences. then, the above two scores are normalized to 0 mean and 1.0 standard deviation and fused by addition calculation in pairs. a word pair is determined as interlingual homograph or cognate if its fusion score is below or above the average score of all form-identical words in the dataset consisting of the same number of homographs and cognates. ## conclusion we integrate word embedding similarity into degree of co-occurrence in parallel sentences to automatically execute interlingual homograph recognition in different languages. we perform it on two language pairs, i.e., chinese-japanese and english-dutch, and the experimental results exhibit the effectiveness of our method. by supplement of the degree of co-occurrence information, the performance of all embeddings can be improved. among all settings, the combination of fasttext and jaccard achieve the best performance in both language pairs. in this work, we focus on interlingual homographs with explicit meaning disparity. however, form-identical words with partially overlapped meanings also exist between some language pairs and we will investigate them for future work.