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 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]: We create a new manually annotated multimodal hate speech dataset formed by $150,000$ tweets, each one of them containing text and an image. We call the dataset MMHS150K, and made it available online . In this section, we explain the dataset creation steps. 
 Question: How many tweats does MMHS150k contains, 150000?
[EX A]: $150,000$ tweets

[EX Q]: The DBpedia SPARQL endpoint is used for query answering and reasoning. 
 Question: What is the reasoning method that is used?
[EX A]:
SPARQL