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| "title": "A Parallel Corpus-Driven Approach to Bilingual Oenology Term Banks: How Culture Differences Influence Wine Tasting Terms", |
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| "first": "Vincent", |
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| "last": "Xian", |
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| "first": "Wang", |
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| "Xi" |
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| "last": "Chen", |
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| { |
| "first": "Songnan", |
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| "email": "songnan.quan@connect.polyu.hk" |
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| "first": "Chu-Ren", |
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| "last": "Huang", |
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| "abstract": "This paper describes the construction of an English-Chinese Parallel Corpus of wine reviews and elaborates on one of its applications-i.e. an E-C bilingual oenology term bank of wine tasting terms. The corpus is sourced from Decanter China, containing 1211 aligned wine reviews in both English and Chinese with 149,463 Chinese characters and 66,909 English words. It serves as a dataset for investigating crosslingual and cross-cultural differences in describing the sensory properties of wines. Our log-likelihood tests revealed good candidates for the Chinese translations of the English words in wine reviews. One of the most challenging features of this domain-specific bilingual term bank is the dominant many-to-many nature of term mapping. We focused on the one-to-many English-Chinese mapping relations of two major types: (a) the words without a single precise translation (e.g. \"palate\") and (b) the words that are underspecified and involve 'place-holder' translation (e.g. \"aroma\"). Our study differs from previous bilingual CompuTerm studies by focusing on an area where cultural and sensory experiences favour many-to-many mappings instead of the default one-to-one mapping preferred in scientific and jurisprudential areas. This necessity for many-to-many mappings in turn challenges the basic design feature of many state-of-the-art automatic bilingual term-extraction approaches.", |
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| "text": "This paper describes the construction of an English-Chinese Parallel Corpus of wine reviews and elaborates on one of its applications-i.e. an E-C bilingual oenology term bank of wine tasting terms. The corpus is sourced from Decanter China, containing 1211 aligned wine reviews in both English and Chinese with 149,463 Chinese characters and 66,909 English words. It serves as a dataset for investigating crosslingual and cross-cultural differences in describing the sensory properties of wines. Our log-likelihood tests revealed good candidates for the Chinese translations of the English words in wine reviews. One of the most challenging features of this domain-specific bilingual term bank is the dominant many-to-many nature of term mapping. We focused on the one-to-many English-Chinese mapping relations of two major types: (a) the words without a single precise translation (e.g. \"palate\") and (b) the words that are underspecified and involve 'place-holder' translation (e.g. \"aroma\"). Our study differs from previous bilingual CompuTerm studies by focusing on an area where cultural and sensory experiences favour many-to-many mappings instead of the default one-to-one mapping preferred in scientific and jurisprudential areas. This necessity for many-to-many mappings in turn challenges the basic design feature of many state-of-the-art automatic bilingual term-extraction approaches.", |
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| "text": "The textual data of wines in the field of wine informatics are increasingly accessible to the public through the Internet in this big data era. Wine reviews have been much criticised in terms of the use of metaphors for describing winetasting experience that often goes so free that it becomes \"difficult to understand\" (Demaeker, 2017, p.117) . However, Croijmans, Hendrickx, Lefever, Majid and Van Den Bosch (2020) refuted this line of criticism by exhibiting high consistency in the use of wine terms in 76,410 wine reviews they gathered, which also effectively trained a classifier that automatically and rather accurately predicted the wine colour (red, white, or rose) and grape variety (n=30) in the wine reviews that were new to the classifier. Largely consistent with the results of Croijmans et al. (2020) , L\u00f3pez-Arroyo and Roberts (2014) found wine reviews used a limited repertoire of commonly-used words to convey specialised senses about wine tasting experience, while extending metaphorical applications of the words. The sensory experience of wine tasting was also studied in the frame of \"motions\" by Caballero (2017) , who drew on cognitive linguistic research on motion events to examine the description of the aromas and flavours of wine \"travelling\" to sensory organs. Caballero gathered 12,000 wine notes in both English and Spanish and identified similarities and differences in the description of motions between the two languages.", |
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| "text": "(Demaeker, 2017, p.117)", |
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| "text": "Croijmans, Hendrickx, Lefever, Majid and Van Den Bosch (2020)", |
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| "section": "Introduction", |
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| "text": "Research in sensory sciences and informatics focuses on the extracting meaningful information from the wine reviews. For example, Valente, Bauer, Venter, Watson and Nieuwoudt (2018) introduced a new approach of using formal concept lattices to visualise the sensory attributes of Chenin blanc and Sauvignon blanc wines. Palmer and Chen (2018) employed a large-scale dataset of wine reviews to perform regression predictions on the grade and price of wines. In linguistic studies, wine reviews provide sensory descriptions for the research on language and cognition. Thus, comparative studies based on bilingual wine reviews, such as Chinese and English, underline the issue of how sensory cognition is encoded across different languages.", |
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| "section": "Introduction", |
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| "text": "Another possible research application is to build English-Chinese parallel corpus based on wine reviews for domain-specific machine translation or translation studies. Such corpora should follow established guidelines (e.g. Chang, 2004) in order to be sharable. Such a corpus is crucial as terms loaded with rich cultural tradition tend to be considered 'untranslatable'. In computational term banks, it often leads to oneto-many (cf. Lim, 2018 Lim, , 2019 , many-to-one, or many-to-many mappings, although the mapping relations do not seem to have attracted in-depth research in computational terminology. In this paper, we propose a parallel corpus-driven approach to culturally bound bilingual terms discovery. In particular, we look at English-Chinese bilingual wine-tasting terminology. Since modern table wine culture and technology are mostly borrowed in the direction from the Western world to China, we focus on the one-tomany mapping of terms in E-C wine terminology. Therefore, this paper will address: a) how we are constructing the English-Chinese parallel corpus of wine reviews; b) the application of this parallel corpus in computational E-C oenology terminology.", |
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| "text": "Chang, 2004)", |
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| "section": "Introduction", |
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| "text": "Another important issue in computational terminology that our study will raise lies in the design criteria and evaluation metrics. The assumed ideal world criterion in bilingual term extraction is to achieve perfect one-to-one mapping. Previous studies on formal information dominant domains (sciences, technology, law etc.) worked well under this default criterion. However, what happens when the best terms in the target language vary in a wide range according to the context? Is there a better algorithm for this complex mapping issue? ", |
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| "section": "Introduction", |
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| "text": "We describe our parallel corpus in terms of the source data (cf. 2.1) and our corpus construction method (cf. 2.2).", |
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| "section": "The Parallel Corpus", |
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| "text": "Our data consists of bilingual reviews published on DecanterChina.com ( \u9187 \u9274 \u4e2d \u56fd ch\u00fan ji\u00e0n zh\u014dnggu\u00f3), a website (www.decanterchina.com) presented by Decanter magazine, an international wine authority. Each wine review (\u9152\u8bc4 ji\u01d4 p\u00edng)", |
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| "section": "Source Data", |
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| "text": "is presented in both English and Chinese (cf. Figures 1 and 2) . One of the present authors who is an accredited English-Chinese translator in China studied the bilingual reviews and confirmed that the Chinese reviews were the human translations of the English ones, ruling out the possibility that they were the outcome of automatic machine translation.", |
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| "text": "Figures 1 and 2)", |
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| "text": "We crawled the data by means of \"request\" and \"Beautiful Soup\" of Python. The website contains data on thousands of wines and each wine has a separate introduction page. Each page displays the name, score, region, grape variety, producer, alcohol level, reference price, and reviews of the wine, which are the focus of this study. By targeting the English and the corresponding Chinese URLs with the use of the English/Chinese switch button on the top right corner of each webpage, we wrote the scripts to simulate the process of clicking on each wine page, and automatically collected the content in each page (Figure 3) . We saved the content into data frame (Figure 4) , and manually removed the noise and inequivalent pairs in the data. ", |
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| "text": "(Figure 3)", |
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| "text": "(Figure 4)", |
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| "section": "Source Data", |
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| "text": "The construction of our corpus is still in progress. The textual attributes of this corpus are from the title data we crawled, namely wine name, score, region, grape, producer, alcohol. These attributes will be valuable for future research on regression analysis of these attributes with wines. The word segmentation and POS tag were conducted by \"Jieba\" for Chinese texts and NLTK for English texts. So far, this parallel corpus is aligned at the paragraph level. We found it was difficult to establish the correspondence between the English texts and their Chinese translations at the sentence level because the wine reviews were rendered rather freely with translation methods like omission, addition, division and combination. We are seeking reliable means for sentence alignment in further studies. This corpus adopted the XML-based framework. The text head consists of the textual attributes and the text body is comprised of the wine reviews and the linguistic tags. This corpus contains 1211 aligned items of English-Chinese wine reviews with 149,463 Chinese characters and 66,909 English words up to now. Although Decanter China published the wine data on its website to the public, such commercial content is typically not easy for others to have the right for redistribution. Thus, we are making an interface for people to access/search in the corpus for academic purpose only without openly sharing it. In addition, we are sharing one of our applications of this corpus, namely the English-Chinese bilingual oenology term bank (Chen, Quan, Wang, & Huang, 2020) , which will be discussed in the following section.", |
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| "text": "We generated two word clouds of our parallel corpus of wine reviews in Chinese and English separately by Nvivo 12 Plus (Figures 5 and 6 ). The full lists of Chinese and English top 100 frequent words are in Appendices (cf . Tables 2 and 3 ). Figures 5 and 6 show that a number of the most frequently used words in the two languages do not match. For instance, there is not a single corresponding item in the Chinese word cloud for \"Palate\" in English, although, physically, \"Palate\" refers to \u816d \u00e8 in Chinese. ", |
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| "text": "(Figures 5 and 6", |
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| "text": ". Tables 2 and 3", |
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| "text": "Figures 5 and 6", |
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| "section": "Identifying the Key Words", |
| "sec_num": "3.1" |
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| "text": "In order to examine the English-Chinese translation correspondence of certain words for the wine reviews, we used an alignment method to detect the word-pairs by the log-likelihood ratio estimation demonstrated in Rapp (1999) . This calculation is based on the assumption according to the Distributional Hypothesis (Harris, 1954) Since the corpus is already aligned in terms of English-Chinese pairs at the paragraph level, we can directly process the word alignment part. The word-level co-occurrence frequency was calculated and a statistical test for the loglikelihood ratio was launched. We desired to do the sentence alignment, but the way to segment a paragraph into the sentences is often different between Chinese and English. We excluded the stop words in both English and Chinese for the purpose of better decreasing the noise. We calculated the log-likelihood ratio for every possible pair of English-Chinese words in the corresponding wine review and sorted them according to the log-likelihood score.", |
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| "text": "As a result, we automatically extracted a bilingual lexicon from the parallel corpus on wine reviews. Four most frequently used words in the word clouds -i.e., \"palate\", \"nose\", \"notes\" and \"aromas\" -are presented in this section with their translation equivalents. Plural forms are used for \"notes\" and \"aromas\" here, since their singular forms occur at very low frequencies in our data. Table 1 lists each word with its top 10 scored candidates, and we manually selected the 'accepted translations' from the top 10 based on their potential to serve as optional translations in wine reviews. The full list of our result, i.e. the English-Chinese bilingual oenology term bank (Chen et al., 2020) can be viewed at https://drive.google.com/file/d/1LlDuU0euWKs zq_WE1eUdzua25m6v3J4X/view?usp=sharing.", |
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| "text": "First, the literal translation of \"palate\" is \"\u816d\" \u00e8 in Chinese, which sounds odd for reviewing wines in Chinese culture as native speakers do not directly mention this sensory part to describe their wine-tasting experience. There are two acceptable translations of \"palate\" in the candidates, namely \"\u98ce\u683c\" f\u0113ng g\u00e9 (style) and \"\u53e3\u5473\" k\u01d2u w\u00e8i (taste), which are rather free renditions. The differences in cultural and sensory experiences between English and Chinese favour this one-to-many mapping instead of the default one-to-one mapping. Second, the word \"nose\" can be either rendered very generally into \"\u9999\u6c14\" xi\u0101ng q\u00ec (scent), or translated literally into \"\u9f3b\u8154\" b\u00ed qi\u0101ng (nasal cavity), which preserves the semantic meaning of nose. Third, there are no acceptable translations of \"notes\" from the top candidates, and the loglikelihood scores of \"notes\" are not ideal. This points to a void in the Chinese lexis that corresponds to the meaning of \"notes\". Finally, \"aromas\" tends to be rendered into \"\u82ac\u82b3\" f\u0113n fang (fragrance), a very acceptable translation that beautifully conveys the meaning of aroma/s.", |
| "cite_spans": [], |
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| "sec_num": "3.2" |
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| "text": "Our appliance of the log-likelihood leads to rather effective identification of good translation candidates for the English words in wine reviews, e.g., the translations for \"palate\", \"nose\" and \"aromas\". However, there were also cases in which not a single acceptable translation can be found -e.g., \"notes\" -which strongly suggest cross-linguistic and cross-cultural differences in word choices for wine reviews. The loglikelihood tests demonstrate that, based on the key words in wine reviews, translation candidates can be generated that are potentially useful for rendering the terms from English into Chinese (cf. 'acceptable translations' in Table 1 ). The translation candidates need to be manually selected to suit various oenological contexts though. Moreover, this method can be applied to the studies of the one-to-many and further manyto-many bilingual term extraction in the domain of oenology regarding the cross-lingual and cross-cultural differences. It can also involve translation studies that look into translation strategies -e.g. literal versus liberal, semantic versus communicative, or foreignised versus domesticated translation -in dealing with the specific texts of wine reviews and the manipulation of translators.", |
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| "sec_num": "3.2" |
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| "text": "Based on the automatic extraction of mapping terms, two major types of mapping of words across the languages emerged. The first type of mapping pertains to the words that have no precise translation equivalent/s (e.g. \"palate\" and \"nose\"), and therefore paraphrasing and other freer translation methods tend to be used. The second type involves 'place-holder' translation, while the multiple mappings are mostly dependent on the modifiers of the word in question to express different meanings. The second type exhibits two sub-types. The first subtype is those that have null term mapping (e.g. \"notes\"). The term is so generic and flexible to collocate with a rich repertoire of modifiers that it is considered as a noun that is semantically bleached and is usually not translated, since the meaning is conveyed by the modifier/s of notes. The second sub-type (e.g. \"aromas\") is still treated as semantically bleached but there is a corresponding 'light noun' -i.e., \u82ac\u82b3 f\u0113n fang 'fragrance' -for direct (one-to-one) term mapping. Our subsequent task is to sort out a solution to automatically classify the three different types and represent these three different types of bilingual term mapping in a term bank.", |
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| "section": "Key Words Translation", |
| "sec_num": "3.2" |
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| "text": "In this paper, we introduced our English-Chinese parallel corpus of wine reviews and described our preliminary attempt for the extraction of bilingual oenology term bank. Our study showed that the log-likelihood approach we chose can deal with the many-to-many mapping challenge posed by the nature of 'untranslatable' terms. Yet it does require significant human interventioni.e., manual selection of the useful translation candidates that suit different oenological contexts. On the other hand, the current corpus size is too small to support deep learning approaches. In the subsequent studies we will enlarge the corpus and also adopt a sensory domain based (rather than term-based) mapping to attempt more revealing findings. ", |
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| "section": "Conclusion", |
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| "text": "We are thankful to the two anonymous reviewers of ", |
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| "ref_entries": { |
| "FIGREF0": { |
| "num": null, |
| "text": "A Wine Review at Decanter (English)", |
| "uris": null, |
| "type_str": "figure" |
| }, |
| "FIGREF1": { |
| "num": null, |
| "text": "A Wine Review at Decanter (Chinese)", |
| "uris": null, |
| "type_str": "figure" |
| }, |
| "FIGREF2": { |
| "num": null, |
| "text": "Data Crawl", |
| "uris": null, |
| "type_str": "figure" |
| }, |
| "FIGREF3": { |
| "num": null, |
| "text": "Figure 4: Data Frame", |
| "uris": null, |
| "type_str": "figure" |
| }, |
| "FIGREF4": { |
| "num": null, |
| "text": "Chinese Word Cloud Figure 6: English Word Cloud", |
| "uris": null, |
| "type_str": "figure" |
| }, |
| "TABREF0": { |
| "content": "<table><tr><td>English</td><td>Acceptable</td><td>Top 10 Scored</td></tr><tr><td>Word</td><td>Translation</td><td>Candidates</td></tr><tr><td/><td/><td>\u98ce\u683c f\u0113ng g\u00e9 (style)\uff1b\u73b0</td></tr><tr><td/><td/><td>\u5728 xi\u00e0n z\u00e0i (now)\uff1b\u820c\u5c16</td></tr><tr><td/><td/><td>sh\u00e9 ji\u0101n (tongue tip)\uff1b\u975e</td></tr><tr><td/><td>\u98ce\u683c f\u0113ng</td><td>\u5e38 f\u0113i ch\u00e1ng (very)\uff1b\u5f88</td></tr><tr><td>Palate</td><td>g\u00e9 (style); \u53e3\u5473 k\u01d2u</td><td>h\u011bn (very)\uff1b\u53e3\u5473 k\u01d2u w\u00e8i (taste)\uff1b\u6df7\u917f h\u00f9n ni\u00e0ng</td></tr><tr><td/><td>w\u00e8i (taste)</td><td>(blend)\uff1b\u8fd9\u6b3e zh\u00e8 ku\u01cen</td></tr><tr><td/><td/><td>(this type)\uff1b\u5e26 d\u00e0i</td></tr><tr><td/><td/><td>(with)\uff1b\u917f\u9020 ni\u00e0ng z\u00e0o</td></tr><tr><td/><td/><td>(brew)</td></tr><tr><td/><td/><td>\u9999\u6c14 xi\u0101ng q\u00ec (scent)\uff1b\u63a8</td></tr><tr><td/><td/><td>\u8350\u4eba tu\u012b ji\u00e0n r\u00e9n</td></tr><tr><td/><td>\u9999\u6c14</td><td>(recommender)\uff1b\u975e\u5e38 f\u0113i</td></tr><tr><td/><td>xi\u0101ng q\u00ec</td><td>ch\u00e1ng (very)\uff1b\u6b3e ku\u01cen</td></tr><tr><td/><td>(scent)\uff1b</td><td>(type)\uff1b\u9ed1\u6bd4\u8bfa h\u0113i b\u01d0 nu\u00f2</td></tr><tr><td>Nose</td><td>\u9f3b\u8154 b\u00ed</td><td>(Pinot Noir)\uff1b\u9152\u5e84 ji\u01d4</td></tr><tr><td/><td>qi\u0101ng</td><td>zhu\u0101ng (winery)\uff1b\u9f3b\u8154 b\u00ed</td></tr><tr><td/><td>(nasal</td><td>qi\u0101ng (nasal cavity)\uff1b\u6f5c\u529b</td></tr><tr><td/><td>cavity)</td><td>qi\u00e1n l\u00ec (potential)\uff1b\u8fd9\u6b3e</td></tr><tr><td/><td/><td>zh\u00e8 ku\u01cen (this type)\uff1b\u53e3\u5473</td></tr><tr><td/><td/><td>k\u01d2u w\u00e8i (taste)</td></tr><tr><td/><td/><td>\u51e0\u5e74 j\u01d0 ni\u00e1n (several</td></tr><tr><td/><td/><td>years); \u4e0d\u6ee1 b\u00fa m\u01cen</td></tr><tr><td/><td/><td>(dissatisfied)\uff1b \u6728\u69ff\u82b1</td></tr><tr><td/><td/><td>m\u00f9 j\u01d0n hu\u0101 (hibiscus)\uff1b\u7b26</td></tr><tr><td/><td/><td>\u5408 f\u00fa h\u00e9 (correspond)\uff1b\u6c34</td></tr><tr><td>Notes</td><td>nil</td><td>\u5e73 shu\u01d0 p\u00edng (level)\uff1b\u6708</td></tr><tr><td/><td/><td>\u520a yu\u00e8 k\u0101n (monthly)\uff1b\u6742</td></tr><tr><td/><td/><td>\u5fd7 z\u00e1 zh\u00ec (magazine)\uff1b\u660e</td></tr><tr><td/><td/><td>\u661f m\u00edng x\u012bng (star)\uff1b\u575a\u56fa</td></tr><tr><td/><td/><td>ji\u0101n g\u00f9 (firm)\uff1b\u751c\u6912 ti\u00e1n</td></tr><tr><td/><td/><td>ji\u0101o (sweet pepper)</td></tr><tr><td/><td/><td>\u5f3a\u58ee qi\u00e1ng zhu\u00e0ng</td></tr><tr><td/><td/><td>(strong)\uff1b\u9ed1\u8272 h\u0113i s\u00e8</td></tr><tr><td/><td/><td>(black)\uff1b\u82ac\u82b3 f\u0113n fang</td></tr><tr><td/><td/><td>(fragrance)\uff1b\u9ec4\u6cb9 hu\u00e1ng</td></tr><tr><td/><td>\u82ac\u82b3 f\u0113n</td><td>y\u00f3u (butter)\uff1b\u975e\u5e38 f\u0113i</td></tr><tr><td>Aromas</td><td>fang</td><td>ch\u00e1ng (very)\uff1b\u5b8c\u7f8e w\u00e1n</td></tr><tr><td/><td>(fragrance)</td><td>m\u011bi (perfect)\uff1b\u5e73\u8861 p\u00edng</td></tr><tr><td/><td/><td>h\u00e9ng (balanced)\uff1b\u7ea2\u679c</td></tr><tr><td/><td/><td>h\u00f3ng gu\u01d2 (red fruit)\uff1b\u5341\u5206</td></tr><tr><td/><td/><td>sh\u00ed f\u0113n (very)\uff1b\u8349\u6728 c\u01ceo</td></tr><tr><td/><td/><td>m\u00f9 (vegetation)</td></tr></table>", |
| "text": "-i.e. word meaning depends on its textual context. Hence, in the parallel corpus, if an English word and a Chinese word co-occur frequently in the parallel sentences, they are potentially good translation candidates for each other(Samuelsson & Volk, 2007).", |
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| "content": "<table><tr><td colspan=\"4\">Appendix B. The English Top 100 Frequent</td></tr><tr><td>Words</td><td/><td/><td/></tr><tr><td/><td>N of</td><td/><td>Weighted</td></tr><tr><td>Word</td><td/><td>Freq</td><td/></tr><tr><td/><td>characters</td><td/><td>percentage (%)</td></tr><tr><td>palate</td><td>6</td><td>692</td><td>3.01</td></tr><tr><td>fruit</td><td>5</td><td>588</td><td>2.56</td></tr><tr><td>nose</td><td>4</td><td>482</td><td>2.10</td></tr><tr><td>aromas</td><td>6</td><td>334</td><td>1.46</td></tr><tr><td>wine</td><td>4</td><td>311</td><td>1.35</td></tr><tr><td>oak</td><td>3</td><td>299</td><td>1.30</td></tr><tr><td>finish</td><td>6</td><td>294</td><td>1.28</td></tr><tr><td>ripe</td><td>4</td><td>266</td><td>1.16</td></tr><tr><td>tannins</td><td>7</td><td>264</td><td>1.15</td></tr><tr><td>black</td><td>5</td><td>232</td><td>1.01</td></tr><tr><td>notes</td><td>5</td><td>232</td><td>1.01</td></tr><tr><td>acidity</td><td>7</td><td>217</td><td>0.95</td></tr><tr><td>fresh</td><td>5</td><td>210</td><td>0.91</td></tr><tr><td>sweet</td><td>5</td><td>193</td><td>0.84</td></tr><tr><td>red</td><td>3</td><td>171</td><td>0.74</td></tr><tr><td>well</td><td>4</td><td>164</td><td>0.71</td></tr><tr><td>fruits</td><td>6</td><td>156</td><td>0.68</td></tr><tr><td>cherry</td><td>6</td><td>151</td><td>0.66</td></tr><tr><td>flavours</td><td>8</td><td>149</td><td>0.65</td></tr><tr><td>spice</td><td>5</td><td>144</td><td>0.63</td></tr><tr><td>style</td><td>5</td><td>135</td><td>0.59</td></tr><tr><td>dark</td><td>4</td><td>125</td><td>0.54</td></tr><tr><td>juicy</td><td>5</td><td>120</td><td>0.52</td></tr><tr><td>long</td><td>4</td><td>118</td><td>0.51</td></tr></table>", |
| "text": "The Chinese Top 100 Frequent Words", |
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| "num": null |
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| "TABREF4": { |
| "content": "<table/>", |
| "text": "The English Top 100 Frequent Words", |
| "html": null, |
| "type_str": "table", |
| "num": null |
| } |
| } |
| } |
| } |
