In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.

Q: The experimental results are shown in Table 1, where it can be seen that autoencoders outperformed MLP and CNN outperformed autoencoders with the highest achieved accuracy of 82.6%.
Question: What was their performance on the dataset?

A: accuracy of 82.6%
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Q: Our novelties include:

Using self-play learning for the neural response ranker (described in detail below).

Optimizing neural models for specific metrics (e.g. diversity, coherence) in our ensemble setup.

Training a separate dialog model for each user, personalizing our socialbot and making it more consistent.

Using a response classification predictor and a response classifier to predict and control aspects of responses such as sentiment, topic, offensiveness, diversity etc.

Using a model predictor to predict the best responding model, before the response candidates are generated, reducing computational expenses.

Using our entropy-based filtering technique to filter all dialog datasets, obtaining higher quality training data BIBREF3.

Building big, pre-trained, hierarchical BERT and GPT dialog models BIBREF6, BIBREF7, BIBREF8.

Constantly monitoring the user input through our automatic metrics, ensuring that the user stays engaged.
Question: What is novel in author's approach?

A: They use self-play learning , optimize the model for specific metrics, train separate models per user, use model and response classification predictors, and filter the dataset to obtain higher quality training data.
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Q: It is interesting to observe that the baseline model amplifies the bias in the training data set as measured by INLINEFORM0 and INLINEFORM1 . From measurements using the described bias metrics, our method effectively mitigates bias in language modelling without a significant increase in perplexity.
Question: how is mitigation of gender bias evaluated?

A:
Using INLINEFORM0 and INLINEFORM1
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