W iktionary Normalization of Translations and Morphological Information (2020)

Abstract: We extend the Yawipa Wiktionary Parser (Wu and Yarowsky, 2020) to extract and normalize translations from etymology glosses, and morphological form-of relations, resulting in 300K unique translations and over 4 million instances of 168 annotated morphological relations. We propose a method to identify typos in translation annotations. Using the extracted morphological data, we develop multilingual neural models for predicting three types of word formation—clipping, contraction, and eye dialect—and improve upon a standard attention baseline by using copy attention.

## introduction wiktionary is a large, free multilingual dictionary with a wealth of information. yawipa @xcite , henceforth w&y, is a recent wiktionary parser billed as "comprehensive and extensible." it has the ability to extract numerous types information from wiktionary, including pronunciations, part of speech, translations, etymology, and a wide range of word relations, and normalize it into an easy to process tabular format. in particular, one of yawipa's innovations over existing parsers was extracting translations from the definition section of a dictionary definition. confirming its easy extensibility and improving upon its comprehensiveness, we extend yawipa's extraction and normalization of wiktionary in two directions: we extract translations from an unusual source, etymology glosses, and we extract morphological relations as annotated by form-of relations. this results in an addition of 282,092 new unique translations and 4,027,201 extracted morphological relations (from the 2020-04 english wiktionary xml dump). we present an analysis that enables us to find typos in translation annotations. using the extracted morphological data, we experiment with several new low-resource (1.5k instances) multilingual prediction tasks on clipping, contraction, and eye dialect. our experiments with neural sequence-tosequence models show that using copy attention can improve performance by up to 52% over a model with a standard attention mechanism. ## related work though wiktionary has existed since 2002, only until very recently has there been a surge of interest in using wiktionary. @xcite was one of the first to examine wiktionary as a resource for nlp. this paper builds upon yawipa @xcite , an open-source, extensible wiktionary parsing framework written in julia with support for parsing a wide variety of data from multiple language editions of wiktionary into a structured machine-readable format. yawipa's goal is to be comprehensive and extensible. to that end, yawipa goes beyond existing parsers in extracting and normalizing information, such as etymology and translations, that exist outside of structured wiktionary markup (we further this goal in this paper), and it facilitates the creation of new parsers for other wiktionary editions. in the literature, there are similar wiktionary parsing efforts (e.g. knowitiary @xcite , dbnary @xcite , and englawi @xcite ), but with different goals and coverage. most studies on translation extraction have utilized the translation section of an entry: ács (2014) using a triangulation approach, @xcite for morphophological analysis, and @xcite as part of a comprehensive wiktionary parsing effort. dbnary @xcite is a similar effort at parsing certain lexical data, including translations, from wiktionary into a structured format. regarding extracting morphological relations between words, the foremost effort is unimorph @xcite @xcite , a large broad-coverage resource comprising morphological paradigms of nouns, adjectives, and verbs in 118 languages extracted from wiktionary. other large-scale parsing efforts for targeted tasks include nulex @xcite for parsing, iwnlp @xcite for lemmatization, and wikipron @xcite for pronunciations. related to the word formation mechanisms we examine, @xcite examine word formation in slang, specifically blends, clippings, and reduplication, and @xcite predict clipping using a lsa-based approach. contractions are not typically studied in a predictive context; @xcite disambiguates contractions as a preprocessing step in machine translation. researchers have recently examined eye dialect in the context of spelling correction @xcite ), but to our knowledge, this paper is the first study on eye dialect generation. ## extracting translations from etymology glosses wiktionary contains translations in a specialized translation section. w&y extract these translations, as well as "translations" from the definition section of non-english word entries. since non-english words have english definitions (in the english wiktionary), short definitions can be regarded as viable translations. one unusual but particularly fruitful source of translations that has not been previously considered is glosses in the etymology section of an entry. for example, in wiktionary the etymology of the german word marienkäfer 'ladybug' is: from maria (given name) + käfer ("beetle"). glosses of each component of the compound word are given in parentheses; these are the translations that we extract. the provided glosses can help disambiguate the word in cases where a word may have multiple senses (e.g. käfer can refer to a beetle, a wench, or the volkswagen car). the decomposition of marienkäfer in the above etymology entry is encoded in mediawiki markup as {{compound|de|maria|@xmath0. this is a wiktionary template with arguments separated by pipes, indicating (1) the word is a compound, (2) it is a german word, (3) the 1 st component is maria, (4) the part of speech of the 1 st component is "given name", (5) the 2 nd component is käfer, and (6) the translation of the 2 nd component is "beetle". from this example, we would extract and normalize the second component's translation to augment the translations already extracted by yawipa from other sources. analysis ## extracting morphological information wiktionary is also a rich source of morphological information. here we focus on one type of information, which we call "form-of relations" because they are annotated in wiktionary using form-of templates. 2 we extract 4,027,201 relations across 168 relation types, a full histogram of which is in appendix a. while different relations have different requirements as to where they can appear in an entry (e.g. some relations can only appear in the etymology section), form-of relations are relatively straightforward to extract and normalize due to the consistency of their templates. many inflectional relations for both nouns and verbs, including relations such as inflection-of, genetivesingular-of, or past-participle-of, are already packaged in unimorph and have been used in tasks such as morphological inflection analysis and prediction @xcite . other relations, such as plural-of and feminine-form-of can augment training data for morphological analysis systems such as that of @xcite . however, much of the rest of this form-of data has not been thoroughly explored. below, we present preliminary experiments on clipping, contraction, and eye dialect, three understudied types of data whose further research is enabled through our extraction and normalization. ## conclusion we extend a yawipa, a comprehensive wiktionary parser, to extract and normalize translations from etymology glosses and morphological form-of relations, resulting in substantial increases in extracted data. our multilingual neural sequence models trained on very low amounts of data show quite low character edit distance when predicting words formed through clipping, contraction and eye dialect. we show that copy attention works well for tasks where the output is a mutation of the input. we envision our newly extracted data to be extremely valuable to researchers working with multilingual text data. data and code are available at github.com/wswu/yawipa.