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.

[EX 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?
[EX A]: accuracy of 82.6%

[EX Q]: The basic concept is to use a phone-discriminative model to produce frame-level phonetic features, and then use these features to enhance RNN LID systems that were originally built with raw acoustic features. The initial step is therefore feature combination, with the phonetic feature used as auxiliary information to assist acoustic RNN LID. This is improved further, as additional research identified that a simpler model using only the phonetic feature as the RNN LID input provides even better performance. We call this RNN model based on phonetic features the phonetic temporal neural LID approach, or PTN LID. As well as having a simplified model structure, the PTN offers deeper insight into the LID task by rediscovering the value of the phonetic temporal property in language discrimination.  
 Question: What is the main contribution of the paper? 
[EX A]: Proposing an improved RNN model, the phonetic temporal neural LID approach, based on phonetic features that results in better performance

[EX Q]: Compared to single-task baselines, performance improved on the low-resource En-De task and was comparable on high-resource En-Fr task. 
 Question: How big are negative effects of proposed techniques on high-resource tasks?
[EX A]:
The negative effects were insignificant.