!Translate: When You Cannot Cook Up a Translation, Explain (2023)

Abstract: In the domain of cuisine, both dishes and ingredients tend to be heavily rooted in the local context they belong to. As a result, the associated terms are often realia tied to specific cultures and languages. This causes difficulties for non-speakers of the local language and ma- chine translation (MT) systems alike, as it implies a lack of the concept and/or of a plausible translation. MT typically opts for one of two alternatives: keeping the source language terms untranslated or relying on a hyperonym/near-synonym in the target language, provided one exists. !Translate proposes a better alternative: explaining. Given a cuisine entry such as a restaurant menu item, we identify culture-specific terms and enrich the output of the MT system with automatically retrieved definitions of the non-translatable terms in the target language, making the translation more actionable for the final user.

## identification of non-translatable fragments sentences that contain terms or phrases that are out of vocabulary for an mt engine typically yield low-quality mt output. hence, we can use a list of entries (glossary) for regional dish names and ingredients, and adopt a brute force approach to identify non-translatable fragments. we iterate through the glossary in the source language and find the longest match in the input sentence. by using the longest match, we take advantage of glossary entries that may contain the full name of a traditional dish, as opposed to single words for a specific ingredient. the matching algorithm considers variants of a term, i.e. aliases that are contained in each glossary entry, since it is common for regional dishes to have more than one name (usually because the original name was in a local dialect and has since been 'italianised', taking a slightly different form), and either variant can appear in restaurant menus or recipes. while more sophisticated entity-linking models could be used (cf. section 5), this bruteforce approach proved to be enough in the cuisine setting. our glossary is built from wikipedia entries that belong to categories associated with the italian cuisine and from an in-house parallel collection of in order to assess the performance of the alternative approaches to non-translatable fragments identification, three annotators labelled 120 instances -one native speaker of italian and two advanced non-native speakers. after consolidation, 111 text spans were identified as non-translatable. ## acquisition of definitions in order to obtain the necessary definitions, we aim at automatically extracting definitional contexts from the wikipedia, the largest multilingual collection of copyright-free encyclopedic content. we use the italian and english wikipedia dumps from july 2021 and keep only the articles that belong to the italian cuisine, according to their associated categories (cf. our objective is extracting definitional contexts that can explain non-translatable cuisine terms it en articles 2,054 1,923 tokens 780,996 1,170,360 avg. length 380 608 gnudi are gnocchi-like dumplings made with ricotta cheese instead of potato, with semolina. the result is often a lighter, "pillowy" dish, unlike the often denser, chewier gnocchi. gnudi is the tuscan word for "naked" (in standard italian "nudi"), the idea being that these "pillowy" balls of ricotta and spinach (sometimes without spinach, which is also known as ricotta gnocchi) are "nude ravioli", consisting of just the tasty filling without the pasta shell. by tradition, in tuscany, these dumplings are served with burnt butter and sage sauce, sprinkled with parmigiano or pecorino toscano cheese. . . . across languages. aristotle formulated definitional contexts as sequences of type ## the !translate components figure 2 illustrates the architecture of the !translate system, which is composed of the backend and the frontend. ## related work entity linking aims at identifying the unique identity of an entry. this kind of technology is commonly supported on linking text to encyclopedic entries. such a process is also known as wikification, in which the entities are linked to the wikipedia in order to augment the comprehensibility of a text. one of the first approaches was wikify! @xcite , which relied on a combination of steps to perform keywordmatching and disambiguation independently. babelfy @xcite is another alternative, but its approach to word disambiguation targets to identify all concepts which, for our purposes, results in over-identification. in recent approaches, entity linking is modeled with neural models that perform the task of entity finding and linking at once @xcite . through a dual encoder, the model proposed by @xcite can link entities in multiple languages. we do not opt for any of these models because the texts we deals with are brief (e.g., menu entries) and rather than performing an open search, we only need to find matches. the task of extracting definitional contexts is not limited to glossaries and encyclopaediae, but extended to other fields such as ontology learning @xcite ), question answering @xcite and elearning @xcite . most approaches rely on lexico-syntactic patterns @xcite @xcite that require manual annotation and/or manually written rules. a different approach has been taken with the use of word lattices, directed acyclic graphs that represent a segment. @xcite introduced word-class lattices to model textual definitions. ## conclusions and future work we have presented !translate , an application that automatically produces translations combining machine translation, entity linking, and supervised definition retrieval to provide informative translations to users in settings in which machine translation alone is not enough. we have focused on the domain of cuisine, in which terms often lack in the target language and require further descriptions (definitions) to become operational. as part of our ongoing work, we are experimenting with a mt quality estimation (qe) component to optionally direct the translation request to a notification queue component that will post a request to a crowdsourcing-based translation component for those sentences that are deemed difficult to translate automatically, even with the help of a glossary.