Translating Collocations: The Need for Task-driven Word Associations (2020)

Abstract: Existing dictionaries may help collocation translation by suggesting associated words in the form of collocations, thesaurus, and example sentences. We propose to enhance them with task-driven word associations, illustrating the need by a few scenarios and outlining a possible approach based on word embedding. An example is given, using pre-trained word embedding, while more extensive investigation with more refined methods and resources is underway.

## introduction in practical bilingual lexicography, there is an important distinction between context-free and contextsensitive translation. context-free translation refers to the general equivalents in a target language given for a particular headword in a source language; and context-sensitive translation refers to the rendition of a headword appropriately according to its occurrence in a given sentence or context. what lexicographers often do is to first produce many translations of a headword in context, and then distill from them a safest equivalent to be the "direct translation" of the headword in the entry, which could be suitably used in most contexts @xcite . this is in response to the habit of many users of bilingual dictionaries who will take the first equivalent found in the entry and use it without paying much attention to the actual context @xcite . in actual translation, however, plugging in the first equivalent found in a dictionary regardless of the context in front of the translator is exactly what is most discouraged. hence for a bilingual dictionary to be helpful to translators, adequate example sentences should be provided to enlighten users of different rendition possibilities and their appropriateness in a variety of contexts. on the other hand, for a translator to use a bilingual dictionary properly and smartly, one has to possess the skills to access the diverse contexts embedding a certain word and thus a whole range of context-sensitive equivalents in addition to the neutral but probably duller word choices. in this study, we focus on the translation of adj-n collocations from english to chinese, and consider the lexical information demand on the translator's part. in addition to the access means in existing dictionaries, in the form of collocations, thesaurus, and examples, we propose to enhance them with task-driven word associations filtered from pre-trained word embedding. this is expected to achieve three purposes: to extend the coverage of less common collocations, to assist translators in more precise word choices, and to encourage the use of different translation strategies appropriate in specific contexts. ## collocations and translation strategies the translation of collocations has long been an issue (e.g. @xcite , and different languages may not have the same collocations @xcite . in the current discussion, we focus on the translation of english adj-n collocations to chinese, which may seem straightforward at times but could always be challenging when considered from the context-sensitive side. take a simple example like good friend. it can be directly and compositionally rendered as 好朋 友 (@xmath0. while in most cases this would be perfectly fine and most acceptable, in translation teaching we are nevertheless told that there are always other alternatives which may fit the contexts even better, and there are different strategies to achieve equivalence at various levels (e.g. @xcite . this is especially salient for literary translation. hence, if the good friend in the source text refers to more or less a confidant, we may use another chinese word 知己; or if the original emphasises the length and intensity of the friendship, phrases like 相知多年 (literally meaning "mutually know well for many years") and 友情深厚 (literally meaning "friendship is deep and thick") may be used, amongst many other possible renditions. the above example shows that even for a simple adj+n collocation, a translator may need access to thesaural information, or near-synonyms, in both the source and target languages, to make appropriate lexical choices. in addition to paradigmatic associations, syntagmatic associations are also necessary, to find out what words can naturally describe good and long friendship in the target language. moreover, even broader word associations are required to enable other translation strategies like transposition, modulation, and paraphrase, which involve the shift in word class and probably an extension into more culturally specific vocabulary items. these three scenarios are further illustrated below, with reference to the macmillan dictionary foot_1 and the cambridge english-chinese dictionary foot_2 . ## task-driven word associations the issue here is therefore to expand lexical access routes in dictionaries, on top of the thesaural and collocational information as well as example sentences already found therein, to facilitate translators' work and to inspire them of the possibilities for rendition. lexical access is largely concerned with word associations which form the basis of modelling the mental lexicon as a vast network (e.g. @xcite . the interconnection of words in such a network can be used to account for and model various phenomena of the semantic memory like tip-of-the-tongue problem (e.g. @xcite . free word associations include associative relations of different types and strengths. their statistical modelling from large corpora (e.g. @xcite has contributed to lexicography for finding collocations and thesaural groups. there is a class of models and methods under distributional semantics @xcite @xcite , where word senses are represented by means of word co-occurrence vectors. the main assumption is that similar words appear in similar contexts, and by comparing the similarity of the vector spaces, it makes a popular approach for extracting paradigmatically related words (e.g. @xcite @xcite . word embedding @xcite is a vector model among the latest trends. ## ongoing work we have only outlined the steps to be tried, in their primitive and crude forms. but while the means are still under investigation, the ends are clear. the word associations modelled from large corpora may give nice results in certain cases, but in practice the associated words can often be so broadly related that they will simply not be equally activated when a person is performing a particular language task. hence, we need task-driven associations to focus on the most relevant associated words in a given context. in this study, we have used collocation translation as a task and proposed to filter the associations by means of similarity or closeness obtained from word embedding. by doing so, we expect to enhance the lexical access means in dictionaries to assist translators in producing both faithful and fluent renditions. in the present discussion, we only used a pre-trained embedding for some preliminary exploration for one scenario. more extensive work is underway, including the refinement of the method to handle different scenarios, the development of a systematic collocation set for testing, and the use of both english and chinese word embedding in the process.