Color Names Across Languages: Salient Colors and Term Translation in Multilingual Color Naming Models

Home , Color term

EUROVIS 2019/ J. Johansson, F. Sadlo, and G. E. Marai Short Paper

Younghoon Kim, Kyle Thayer, Gabriella Silva Gorsky and Jeffrey Heer

University of Washington

orange brown red yellow

green pink

English gray

black purple blue

갈 빨강 노랑 주황 연두 자주 초록 회 분홍

Korean 청록 보라 하늘 검정 남 연보라 파랑 Figure 1: Maximum probability maps of English and Korean color terms. Each point represents a 10 × 10 × 10 bin in CIELAB color space. Larger points have a greater likelihood of agreement on a single term. Each bin is colored using the average color of the most probable name term. Bins with insufﬁcient data (< 4 terms) are left blank. English has 10 clusters corresponding to basic English color terms [BK69], whereas Korean exhibits additional clusters for 남 ( ), 청록 ( ), 자주 ( ), 하늘 ( ), 연두 ( ), and 연보라 ( ).

Abstract Color names facilitate the identification and communication of colors, but may vary across languages. We contribute a set of human color name judgments across 14 common written languages and build probabilistic models that find different sets of nameable (salient) colors across languages. For example, we observe that unlike English and Chinese, Russian and Korean have more than one nameable blue color among fully-saturated RGB colors. In addition, we extend these probabilistic models to translate color terms from one language to another via a shared perceptual color space. We compare Korean-English translations from our model to those from online translation tools and find that our method better preserves perceptual similarity of the colors corresponding to the source and target terms. We conclude with implications for visualization and future research. CCS Concepts • Human-centered computing → Visualization design and evaluation methods; Visualization systems and tools;

1. Introduction urated hues (with full saturation and brightness in HSV space) across 14 common languages, and names for full colors (the en- Associations between colors and linguistic terms (names) are valu- tire RGB cube) in English and Korean. able to consider when choosing colors for visual communication. Salient colors with unambiguous names make it easier to refer to, • Probabilistic color naming models for each language and color recognize, and recall graphical elements [RDD00]. Prior work con- set. For the saturated hues, we investigate how the most name- tributes color name models in English as well as for unwritten able (salient) colors vary per language. For example, we conﬁrm ∗ languages [CSH08, HS12, KBM 09]. Though speciﬁc instances of that Korean and Russian have two nameable blue colors (하늘 naming and perceptual differences have been studied across lan- and 파랑 , голубой and синий ) in the saturated ∗ guages [GPRMA17,Ath09,ADKS11,WWF 07], we lack multilin- hues [WWF∗07], unlike other languages (e.g., blue in En- gual color naming models to aid visualization and graphic design. glish). We construct color naming models [CSH08] across languages • Application of our multilingual models to perform color term based on crowdsourced color name judgments. We contribute: translation. We compare our results to translations from popular • Two datasets of human color-name judgments: names for sat- English-Korean online translation tools. We discover question-

able translations by the online tools, such as 자주 ( ) for purple subject used the expected character set, so we could prompt them ( ), whereas our method maintains perceptual fidelity. to do so if needed. Our collected data and color naming models are The stimuli colors came from one of two sets: saturated hues, available online at https://github.com/uwdata/ a path along the edge of the HSV color wheel with full saturation color-naming-in-different-languages. (S = 1) and brightness (V = 1), and full colors, colors from the full RGB cube. Initially, we restricted ourselves to the saturated hues 2. Related Work to make data collection more feasible. We chose the hue colors in particular because they are commonly used in color pickers (e.g., The commonality of color names across languages has been studied default pickers in Windows and Mac OS) and we believed these and debated. Universalists argue for innate color perception mech- colors would be more straightforward to name. Once a language anisms, while relativists point to variations in color terms across received 1,000 color-name pairs we began collecting colors from languages and cultures [KR06]. To investigate this issue, Berlin and the full RGB cube for that language. Kay first collected color names across 20 languages from one bilingual speaker per language in the San Francisco Bay area [BK69]. To ensure that each participant is given an approximately per- Their World Color Survey [KBM∗09] then collected color-name ceptually uniform set of colors, we discretize the hue circle into 36 pairs for 110 unwritten languages. In this work, we collect human equally-spaced 36 bins within CIELAB color space. Every subject color-name judgments from multiple native speakers of common saw one color from each of these 36 bins, with the specific color written languages using LabintheWild [RG15], an online platform stimuli randomly sampled from each bin. To sample the RGB cube, for crowdsourced experimentation. we select 36 random colors from the full space, subject to the con- straint that all samples must be at least 20 units apart in CIELAB To model associations between colors and names, Heer & space to ensure that reasonably different colors are presented. Stone [HS12] apply Chuang et al.’s probabilistic modeling ap- proach [CSH08] to over 3M English color name judgments gath- For the color sorting task, we present 90 color tiles to sort. Sub- ered from readers of the web comic XKCD [Mun10]. This model jects sorted 15 tiles at a time, and asked to form a smooth gradation allows calculation of a color’s saliency (the degree of naming con- between anchored starting and ending tiles. This task was inspired sensus for a given color), and replicates identification of Berlin by the Farnsworth-Munsell [Far43] 100-hue and dichotomous tests & Kay’s basic color terms [BK69]. Heer & Stone then apply the for color vision, which involve sorting 100 physical color tiles from model to evaluate color palettes for data visualization. We build the perceptually-based Munsell color space. In our test we chose 90 similar models for multiple languages, and use them to analyze and colors (instead of 100) that were equally spaced along the largest compare color saliency across languages. centered circle in the a*b*-plane of CIELAB.

3. Data Collection 3.2. Recruitment We collected color name judgements for different languages in an To recruit subjects, we posted links to the study on Facebook and experiment on LabintheWild [RG15], an online platform with a Twitter under our own profiles and the official LabintheWild Face- globally diverse user base. Experiments on LabintheWild motivate book page. We also encouraged friends and family to take the test users to participate and share the experiment by allowing them and share their results on social media. To promote more partici- to learn something about themselves and compare themselves to pation, we translated the experiment instructions into Korean, Chi- their peers. Our experiment advertised that participants could learn nese, and Farsi after launching. One later advertising post included about their color perception abilities and compare their results with Korean and English names of some hue colors, which may have others. The study can be taken at https://labinthewild.org/ primed the subjects’ color naming, so we exclude hue color-name studies/color_perception/. pairs collected after this posting. The experiment had three sections: demographic questions (e.g., In total we collected 131k color-term pairs from 4.2k partici- native language), two color perception tasks (color naming and pants across 70 languages from May 27, 2016 to February 1, 2019. sorting), and a results page. The naming task produced the data for In this paper we focus on the 14 languages that had at least 500 re- this study, while the sorting task let us calculate a color perception spondents: English, Korean, German, Spanish, French, Portuguese, score for the results page, so that participants could compare their Swedish, Polish, Russian, Chinese, Persian, Dutch, Finnish, and score with the average and share it on social media. Romanian for the saturated hues. For the full colors, we examine Korean and English, the two languages for which we have suffi- 3.1. Task cient coverage. Data collection remains ongoing. Our experiment had three pages where we asked the participant to name colors. We asked subjects to give names in their native 3.3. Data Processing language, using the most common character set for that language. The free text responses result in a variety of color names. To man- For each color naming stage, a participant was shown 12 color age variations in punctuation, we remove non-alphabetic charac- tiles (for a total of 36 tiles across the three pages). Each tile was ters (e.g., dashes, underscores, and whitespace). We change all up- a 150 × 30 pixel rectangle with a 0.5 pixel black border and white percase letters to lowercase when available. For Russian, Korean, background. Below the tile was a text box where subjects could Chinese, Arabic, and Persian, we filter out responses with non- enter the color name. For Chinese and Korean, we detected if the native script characters. We also exclude color terms that do not

c 2019 The Author(s) Eurographics Proceedings c 2019 The Eurographics Association. Y. Kim et al. / Color Names Across Languages: Salient Colors and Term Translation in Multilingual Color Naming Models belong to the participant’s self-reported language by checking if Color Bins 1.0 they are listed as colors in native dictionaries. Lastly, we apply cus- Spanish 0.5 (1375) tomized rules for Korean, English, Persian, Portuguese, Chinese, 0.0 Saliency 1.0 and French terms that correct typos and merge words that were French 0.5 (1402) grammatically the same. The rules were reviewed by at least one 0.0 1.0 native speaker per language (details in supplemental materials). English 0.5 (25053) 0.0 To aggregate responses, we bin the saturated hues into the 36 1.0 Portuguese 0.5 sampling bins mentioned earlier and bin the full RGB cube into (1020) × × 0.0 10 10 10 bins in CIELAB space. We omit RGB cube bins with 1.0 Romanian 0.5 less than 4 observations. Following Heer & Stone [HS12] we also (617) 0.0 remove sparsely occurring terms to reduce noise: for saturated hues 1.0 Dutch 0.5 we retain the top 20 terms for each language, and for the full colors (581) 0.0 we exclude terms that occur only once. This exclusion drops rela- 1.0 Polish 0.5 tively little information. The Frobenius norm of the color-by-name (967) matrix (columns are terms, rows are color bins) is reduced by less 0.0 1.0 Persian 0.5 than 4% for saturated hues (except for Chinese, reduced by 8%), (717) and less than 1% for full colors. 0.0 1.0 Swedish 0.5 (1150) 4. Analysis & Results 0.0 1.0 Finnish 0.5 (578) The final data set we used to build our probabilistic models had 0.0 1.0 37,763 saturated hue and 97,785 full color records. We now com- German 0.5 (1705) pare the color name compositions of the saturated hues and the full 0.0 1.0 colors across the languages [CSH08]. We then introduce a transla- Russian 0.5 (711) tion model by extending the full color models. 0.0 1.0 Chinese 0.5 (582) 4.1. Color Name Compositions Across Languages 0.0 1.0 Korean 0.5 To compare agreement for color names, we first compute the proba- (1306) 0.0 bility P(T|c) of terms T within a color bin c. To quantify the degree of naming consensus for a given color, we compute two measures: Figure 2: The probabilities of terms for each hue color bin across the negative entropy saliency(c) = −H(P(T|c)), and the maximum 14 languages. Each area represents a term (t) and its height in a bin probability maxProb(c) = max(P(T|c)). Prior work uses the for- (c) represents P(t|c). The color of an area is the average color for mer measure to measure color nameability [CSH08,HS12]. We also the corresponding term. Gray circles below each chart encode each employ the latter measure as it indicates the maximum likelihood bin’s saliency. Larger circles mean that the corresponding color is of agreement on a single term. more likely to be called by a common name. The rows are sorted according to their distributional similarity. In Figure2, we visualize the naming of hue colors for 14 languages (see supplemental materials for interactive visualizations). For the full colors in Korean and English, we observe the name- The figure shows a common pattern in saliency: 7 color regions able color clusters in Figure1. In English, we see 10 evident clus- corresponding to Berlin & Kay’s basic color terms [BK69]: red, ters that match Berlin & Kay’s English basic color terms [BK69], as orange, yellow, green, blue, purple, and pink (weakly), are salient seen in prior name models [HS12]. Meanwhile, the Korean model across languages. However, we also see that some languages have exhibits additional clusters such as, 남 ( near black), 청록 ( additional salient colors: Korean has two nameable blue colors between blue and green), 자주( between purple and red), 하늘 (하늘 and 파랑 ), as does Russian (the previously studied ( ), 연두 ( ), and 연보라 ( between light blue and pink, a голубой and синий [WWF∗07]). In addition, we observe compound of the words 연[slight] and 보라[purple]). We also note a salient Russian teal color (бирюзовый ). that the models do not include a cluster for white, likely due to the In comparison to the other languages, Chinese color name white background (as discussed in previous work [HS12]). saliency was relatively low, exhibiting higher naming variation. This observation may explain why the Chinese dataset saw a 4.2. Translation via Color Naming Model greater reduction (%8) than the other languages (< %4) when We can use our models to quantify the translation quality of cross- culling low-frequency terms. From an informal interview with a language term pairs. We use the color-term probability P(C|t) and native Chinese speaker, we believe this variance is derived from assume that a translation should conserve this probability distribu- the diverse combinations of multiple basic color characters. For ex- tion over color bins. We formulate the translation loss for translat- ample, 蓝绿( ) is 蓝( ) plus 绿( ), 黄绿( ) is 黄( ) plus ing a term ts in a source language ls to another term tt in a target 绿( ). As for the orange hues 橘( ) and 橙( ), they are the language lt as a distance: names of different fruits – mandarins and oranges – and their use may reflect regional linguistic differences. translationLoss(ts,ls,tt ,lt ) = distance(P(C|ts,ls),P(C|tt ,lt ))

We employ the Earth Mover’s Distance [PW08, PW09] within Translation Loss (Kor Eng) Translation Loss (Eng Kor) CIELAB space as our distance metric. With this loss function, the 4 3 2 1 0 0 1 2 3 4 JND red JND best translation for a term t is expressed as: brown translation(t,ls,lt ) = argmin(translationLoss(ts,ls,tt ,lt )). tt orange

To evaluate this model, we compute translations between the top yellow 100 most frequent terms in the Korean and English full color datasets. We then compare our translations to popular English- lightgreen springgreen Korean online translation tools: Google Translate and Papago [LKS∗16]. Figure3 visualizes the translations of the major color green terms (shown in bold) we previously identiﬁed in Figure1. The results reveal some questionable translations by the online skyblue 자주 translation tools: translations from purple ( ) to ( ) (and babyblue vice versa), from 연보라 ( ) to lightpurple ( ), from 남 ( ) to indigo ( ), and from 청록 ( ) to turquoise ( ) or cyan ( ) blue all have translation losses greater than the corresponding just no- cobalt ticeable distance (JND) in CIELAB space. In other words, these purple translated terms are predicted to have a ≥ 50% chance of being perceived differently from the source term. purple magenta

5. Conclusion & Future Work lightpurple lavender Our findings corroborate prior results about differences in nameable colors across languages, for example, that Russian has two pink salient blue colors [WWF∗07]. With our data and models we can discover such color naming patterns across a number of languages. indigo Vega-Lite Clear Format Export Share Examples Gist Vega-Lite Docs Edit navy Vega-Lite Clear Format Export Share The differences we find can be used to further assessExamples theGist generaliz-Vega-Lite Docs Edit 1 { 1 { 2 "$schema": "https://vega.github.io/schema/vega-lite/v3.json", 2 "$schema": "https://vega.github.io/schema/vega-lite/v3.json", ability of language-dependent differences in the perception of col- 3 3 turquoise 4 "width": 300, 4 "width": 300, ∗ 5 "height": 50, 5 "height": 50, ors from previous studies [GPRMA17,Ath09,ADKS11,WWF 07]. teal 6 "mark": "bar", 6 "mark": "bar", 7 "transform": [ 7 "transform": [ 8 { cyan 8 { 9 "calculate": "if(datum.lang==='English (English)',datum.pCT, 9 "calculate": "if(datum.lang==='English (English)',datum.pCT, The differences in nameable colors we observe across languages -datum.pCT)", -datum.pCT)", 10 "as": "newPCT" 10 "as": "newPCT" suggest extensions to prior color palette evaluations based solely on gray 11 }, 11 }, 12 {"filter": "datum.term === ' ' || datum.term === 'green'"} 12 {"filter": "datum.term === ' ' || datum.term === 'green'"} 13 ], 13 ], English color terms [HS12, GLS17]. Color palettes can be adapted 14 "encoding": { 14 "encoding": { black 15 "x": { 15 "x": { 16 "field": "binNum", 16 "field": "binNum", specifically to speakers of different languages. For example, Ko- 17 "type": "ordinal", 17 "type": "ordinal", 18 "scale": {"domain": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]}, 18 "scale": {"domain": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]}, 파랑 синий Translation 19 "axis": null 19 "axis": null rean and Russian have darker blue colors ( and ) 20 }, 20 }, By Prob. Model By Online Translators 21 "y": { 21 "y": { 22 "field": "pCT", as shown in Figure2. A designer might use these darker blues to 22 "field": "pCT", 23 "type": "quantitative", 23 "type": "quantitative", 24 "scale": {"domain": [0, 1]}, 24 "scale": {"domain": [0, 1]},improve color saliency for Korean and Russian audiences. The im- Figure 3: Comparison of translations. Popular terms (bold) in En- 25 "axis": { 25 "axis": { 26 "values": [0], 27 "ticks": false, 26 "values": [0], proved saliency should hopefully promote those viewers’ ability to glish and Korean are translated by the probabilistic model (black 28 "zindex": 1, 27 "ticks": false, 29 "labels": false, 28 "zindex": 1, 30 "titleAngle": 0, 29 "labels": false, reference and remember graphical elements. arrows) and online translators (gray dashed arrows). The circles 31 "titleAlign": "right", 30 "titleAngle": 0, 32 "title": "green(103) / (10)" 31 "titleAlign": "right", 33 } 32 "title": "green(103) / (10)" use the average color for the name, and the bars encode the trans- 34 }, 33 } In addition, our translation model predicts possible misunder- 35 "row":{ 34 }, lation losses. The vertical dashed line denotes approximately 1 just 36 "field":"term" 35 "row":{ 37 }, 36 "field":"term" standings when verbally communicating about data visualizations 38 "color": { 37 }, noticeable difference (JND) in CIELAB space [Sha02]. 39 "field": "rgb", 38 "color": { 40 "legend": null, across different languages. Consider a heat map that uses the viridis 39 Vega-Lite"field": "rgb", Clear Format Export Share Examples Gist Vega-Lite Docs 41 "scale": { 40 "legend": null, Edit 42 "domain": [ 41 "scale": { colormap [SvdW15]. If an English speaker refers to a 43 "rgb(68, 1, 84)", 42 "domain": [ collected demographic data, for example to examine potential dif- 44 "rgb(72, 36, 117)", 43 "rgb(68, 1, 84)", 45 "rgb(65, 68, 135)", 1 { “green” area, they may be referring to a wide range of colors in 46 "rgb(53, 95, 141)", 44 "rgb(72, 36, 117)", ferences according to reported gender. We hope to investigate these green(103) / (10) 47 "rgb(42, 120, 142)", 45 2 "$schema""rgb(65, 68, 135)": "https://vega.github.io/schema/vega-lite/v3.json, ", green 48 "rgb(33, 145, 140)", 46 "rgb(53, 95, 141)", the colormap ( ). If a Korean speaker translates this as green(103) / (10) 47 "rgb(42, 120, 142)", 49 "rgb(34 168 132)" term 3 "vconcat": [ green possibilities as our color naming experiment continues to collect 48 "rgb(33, 145, 140)", 1.0 초록 green(103) / (10) 49 4 {"rgb(34 168 132)" “ ” (following existing onlineterm translation tools), they may only purple (31) green(103) / (10) more data on LabintheWild. 5 "width": 300, consider a0.5 narrower range of colors ( ). To avoid this type 6 "height": 50, of communication0.0 mismatch, visualization designers may wish to 7 "mark": "bar", 1.0 8 consider color name translation losses. 6. Acknowledgements (1) 0.5 9 "encoding": { Looking ahead, there remains a range of improvements that Yvonne Chen and Jingjing Wang helped prototype the experiment 10 "x": { 0.0 11 "field": "binNum"could, be made to our current study. The quality of the data might and visualizations. Angli Liu, Tongshuang Wu, Nara Jung, Nigini 1.0 12 "type": "ordinal"be, further improved, as the cleaning of color terms was supervised Oliveira, Sayena Majlesein, and Yasaman Sefidgar provided trans- blue (42) 0.5 13 "scale": {"domain"by: [0, 1, 2 native, 3, 4, 5, 6, 7, 8, 9, 10], speakers"padding": 0}, for only six of the languages. Collecting more lations of the experiment instructions and helped with data clean- 14 "axis": null 0.0 15 non-English color-term pairs would permit fine-grained analysis ing. We also appreciate feedback and help from Katharina Reinecke 1.0 16 }, for more languages, including translation models. Finally, larger and the WildLab. This work was supported by a Moore Foundation (18) 17 "y": { datasets could0.5 support more nuanced models that also incorporate Data-Driven Discovery Investigator Award. 18 "value": 1 0.0 19 }, 1.0 20 "color": { teal (63) c 2019 The Author(s) 21 "field": "rgb", 0.5 Eurographics Proceedings c 2019 The Eurographics Association. 22 "legend": null, 0.0 23 "scale": { 1.0 24 "domain": [ (10) 25 "rgb(68, 1, 84)", 0.5

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34 "rgb(189, 223, 38)", 0.0 35 "rgb(253, 231, 37)" 1.0 36 ], lightgreen (10) 37 "range": [ 0.5

38 "rgb(68, 1, 84)", 0.0 39 "rgb(72, 36, 117)", 1.0 40 "rgb(65, 68, 135)", (19) 41 "rgb(53, 95, 141)", 0.5

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c 2019 The Author(s) Eurographics Proceedings c 2019 The Eurographics Association.