nerdexam
Google

PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #115

You work for a gaming company that has millions of customers around the world. All games offer a chat feature that allows players to communicate with each other in real time. Messages can be typed…

The correct answer is B. Train a classifier using the chat messages in their original language. Since the performance of the model varies significantly across different languages, it suggests that the translation process might have introduced some noise in the chat messages, making it difficult for the model to generalize across languages. One way to address this issue is…

Submitted by krish.m· Apr 18, 2026Data processing and feature engineering

Question

You work for a gaming company that has millions of customers around the world. All games offer a chat feature that allows players to communicate with each other in real time. Messages can be typed in more than 20 languages and are translated in real time using the Cloud Translation API. You have been asked to build an ML system to moderate the chat in real time while assuring that the performance is uniform across the various languages and without changing the serving infrastructure. You trained your first model using an in-house word2vec model for embedding the chat messages translated by the Cloud Translation API. However, the model has significant differences in performance across the different languages. How should you improve it?

Options

  • AAdd a regularization term such as the Min-Diff algorithm to the loss function.
  • BTrain a classifier using the chat messages in their original language.
  • CReplace the in-house word2vec with GPT-3 or T5.
  • DRemove moderation for languages for which the false positive rate is too high.

How the community answered

(36 responses)
  • A
    14% (5)
  • B
    78% (28)
  • C
    3% (1)
  • D
    6% (2)

Explanation

Since the performance of the model varies significantly across different languages, it suggests that the translation process might have introduced some noise in the chat messages, making it difficult for the model to generalize across languages. One way to address this issue is to train a classifier using the chat messages in their original language.

Topics

#Multilingual ML#Bias & Fairness#Data Preprocessing#NLP

Community Discussion

No community discussion yet for this question.

Sign up to join the discussion
Full PROFESSIONAL-MACHINE-LEARNING-ENGINEER Practice