Examples of using We need to eliminate in English and their translations into Chinese
{-}
-
Political
-
Ecclesiastic
-
Programming
We need to eliminate them.
We need to eliminate poverty.
Now we understand that we need to eliminate managers, not ordinary soldiers.
We need to eliminate the noise.
We need to eliminate that fear.
The egg: do we need to eliminate the external barriers women face getting into such roles in the first place?
We need to eliminate the border.
We need to eliminate deforestation.
We need to eliminate deforestation.
We need to eliminate this possibility.
But we need to eliminate unnecessary assumptions as well.
First, we need to eliminate the existing hierarchy of subjects.
We need to eliminate the gender pay gap as quickly possible.
We need to eliminate greenhouse gas emissions as rapidly as possible.
We need to eliminate duplication in order to relieve the resultant financial and human burden.
We need to eliminate the terrorist and extremist threats that continue in various regions of Afghanistan.
The egg: We need to eliminate the external barriers to get women into those roles in the first place.”.
We need to eliminate killer diseases, such as malaria and HIV, which affect vast segments of our deprived societies.
We need to eliminate tax havens, for we must not tolerate places where money derived from speculation, crime and fraud is stashed.
We need to eliminate this threat to our shared future and we urge you to join us, the Generation of Change.
We need to eliminate the use of conditionalities and allow developing countries the necessary fiscal space to implement national development objectives.
We need to eliminate official corruption, foster a culture of accountability and cultivate the values and institutions that favour enduring democracy and constitutionality.
Together, we need to eliminate misconceptions like“artificial intelligence will replace my work” and show people how machines learn from data, not experience.
Together, we need to eliminate misconceptions like“artificial intelligence will replace my work” and show people how machines learn from data, not experience.