Topic 1 Question 104
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?
Add a regularization term such as the Min-Diff algorithm to the loss function.
Train a classifier using the chat messages in their original language.
Replace the in-house word2vec with GPT-3 or T5.
Remove moderation for languages for which the false positive rate is too high.
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- 正解だと思う選択肢: 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.
👍 5TNT872023/03/09 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 .
👍 3Ml062023/03/03- 正解だと思う選択肢: 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.
👍 3tavva_prudhvi2023/03/27
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