Replication Paper: ggplot multinomial regression with parameters

Hello I am trying to replicate an economics paper. I have the following model that needs to be graphed:

Variables: LNEARN, EDUC, EXP, ysm, and the only binary variable LANG (see below)
LNEARN ~ 0.049EDUC + 0.023EXP - 0.037 (EXP^2/100) + 0.028 ysm - 0.041(ysm^2/100) + LANG + 5.056

Current Code -----------------

m_i <- lm(LNEARN ~ EDUC + EXP + I(EXP^2/100) + ysm + I(ysm^2/100) + LANG, df_clean)

ggplot(df_clean, aes(x = ysm, y = LNEARN, color = LANG, size = EDUC)) +
xlab ("Years of Formal Education") +
ylab("Log of Earnings") +
ggtitle("Education's Potential Impact On Immigrant Earnings") +
labs(subtitle = "1990 US Census Data", color = "Language", size = "Education") +
geom_point() +
# Data-generated regression
geom_smooth() +

I would like to plot this model and its regression of my cleaned data against the paper's estimates of the polynomial regression coefficients using ggplot. geom_smooth() is great and all but I need to compare it to the paper results.

Follow up: If it is important for fixing some of my variables, here are the paper's reported means of the variables that aren't being varied along x and y in my ggplot:

mean reported EXP: 22.76
mean reported ysm: 15.43

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