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Exam Professional Machine Learning Engineer topic 1 question 104 discussion

Actual exam question from Google's Professional Machine Learning Engineer
Question #: 104
Topic #: 1
[All Professional Machine Learning Engineer Questions]

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?

  • A. Add a regularization term such as the Min-Diff algorithm to the loss function.
  • B. Train a classifier using the chat messages in their original language.
  • C. Replace the in-house word2vec with GPT-3 or T5.
  • D. Remove moderation for languages for which the false positive rate is too high.
Show Suggested Answer Hide Answer
Suggested Answer: B 🗳️
by kunal_18 at Dec. 24, 2022, 5:23 p.m.

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TNT87
Highly Voted 1 year, 7 months ago
Selected Answer: B
Answer B 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.
upvoted 9 times
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desertlotus1211
Most Recent 2 months, 1 week ago
Selected Answer: C
The issue is with the language translation - GPT-3 or T5 are trained on large multilingual datasets and are designed to capture the nuances of multiple languages. By replacing your in-house word2vec model with one of these state-of-the-art models, you can leverage their robust, context-aware embeddings to achieve more uniform performance across various languages.
upvoted 1 times
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Zwi3b3l
9 months, 2 weeks ago
Selected Answer: A
uniform performance
upvoted 1 times
pinimichele01
6 months, 3 weeks ago
Adding a regularization term to the loss function can help prevent overfitting of the model, but it may not necessarily address the language-specific differences in performance. The Min-Diff algorithm is a type of regularization technique that aims to minimize the difference between the model predictions and the ground truth while ensuring that the model remains simple. While this can improve the generalization performance of the model, it may not be sufficient to address the language-specific differences in performance. Therefore, training a classifier using the chat messages in their original language can be a better solution to improve the performance of the moderation system across different languages.
upvoted 1 times
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ciro_li
1 year, 3 months ago
Selected Answer: B
Min-diff may reduce model unfairness, but here the concern is about improving performance. Training models avoiding Cloud Natural API should be more suitable.
upvoted 2 times
tavva_prudhvi
1 year, 3 months ago
Adding a regularization term to the loss function can help prevent overfitting of the model, but it may not necessarily address the language-specific differences in performance. The Min-Diff algorithm is a type of regularization technique that aims to minimize the difference between the model predictions and the ground truth while ensuring that the model remains simple. While this can improve the generalization performance of the model, it may not be sufficient to address the language-specific differences in performance. Therefore, training a classifier using the chat messages in their original language can be a better solution to improve the performance of the moderation system across different languages.
upvoted 1 times
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[Removed]
1 year, 3 months ago
Selected Answer: A
A is correct since it encourages the model to have similar performance across languages. B would entail training 20 word2vec embeddings + maintaining 20 models at the same time. On top of that, there would be no guarantee that those models will have comparable performance across languages. This is certainly not something you would do after training your first model.
upvoted 3 times
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friedi
1 year, 4 months ago
Selected Answer: A
A is correct, the key part of the question is „[…] assuring the performance is uniform […]“ which is baked into the Min-Diff regularisation: https://ai.googleblog.com/2020/11/mitigating-unfair-bias-in-ml-models.html
upvoted 2 times
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M25
1 year, 5 months ago
Selected Answer: B
Went with B
upvoted 1 times
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tavva_prudhvi
1 year, 7 months ago
Selected Answer: B
Since the current model has significant differences in performance across the different languages, it is likely that the translations produced by the Cloud Translation API are not of uniform quality across all languages. Therefore, it would be best to train a classifier using the chat messages in their original language instead of relying on translations. This approach has several advantages. First, the model can directly learn the nuances of each language, leading to better performance across all languages. Second, it eliminates the need for translation, reducing the possibility of errors and improving the overall speed of the system. Finally, it is a relatively simple approach that can be implemented without changing the serving infrastructure.
upvoted 4 times
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hakook
1 year, 7 months ago
Selected Answer: A
should be A https://ai.googleblog.com/2020/11/mitigating-unfair-bias-in-ml-models.html
upvoted 2 times
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Ml06
1 year, 8 months ago
B i think is the correct answer C is an overkill , you have just developed your first model you don’t jump into solution like C , in addition the problem is that there is a significant difference between language note the model is enormously underperforming . Finally you are serving millions of users , running chat GPT or T5 for a task like chat moderation (and in real time) is extremely wasteful .
upvoted 3 times
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John_Pongthorn
1 year, 8 months ago
Given that GPT-3 is rival of google , C is not possible certainly .
upvoted 3 times
John_Pongthorn
1 year, 8 months ago
we are taking into account 20 muti classification, it is relevant about FP or FN.
upvoted 1 times
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egdiaa
1 year, 10 months ago
Selected Answer: C
GPT-3 is best for generating human-like Text
upvoted 3 times
lightnessofbein
1 year, 8 months ago
Does "moderate" means we need to generate text?
upvoted 2 times
desertlotus1211
2 months, 1 week ago
yes, what else would you generate when you need to communicate over a messaging system?
upvoted 1 times
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kunal_18
1 year, 10 months ago
Ans : C https://towardsdatascience.com/poor-mans-gpt-3-few-shot-text-generation-with-t5-transformer-51f1b01f843e
upvoted 1 times
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C (25%)
B (20%)
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