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: Using a set of semantically oriented tasks that require explicit semantic cross-lingual representations, we showed that mBERT contextual embeddings do not represent similar semantic phenomena similarly and therefore they are not directly usable for zero-shot cross-lingual tasks. 
 Question: What challenges this work presents that must be solved to build better language-neutral representations?

A: contextual embeddings do not represent similar semantic phenomena similarly and therefore they are not directly usable for zero-shot cross-lingual tasks
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Q: Three different datasets have been used to train our models: the Toronto book corpus, Wikipedia sentences and tweets.  Our Sent2Vec models also on average outperform or are at par with the C-PHRASE model, despite significantly lagging behind on the STS 2014 WordNet and News subtasks. This observation can be attributed to the fact that a big chunk of the data that the C-PHRASE model is trained on comes from English Wikipedia, helping it to perform well on datasets involving definition and news items.  
 Question: Do they report results only on English data?

A: Yes
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Q: Long-short Term Hybrid Memory and Multi-attention Block. Long-short Term Hybrid Memory (LSTHM) is an extension of the Long-short Term Memory (LSTM) by reformulating the memory component to carry hybrid information.  In the LSTHM model, we seek to build a memory mechanism for each modality which in addition to storing view-specific dynamics, is also able to store the cross-view dynamics that are important for that modality. This allows the memory to function in a hybrid manner. 
 Question: What is the difference between Long-short Term Hybrid Memory and LSTMs?

A:
Long-short Term Hybrid Memory (LSTHM) is an extension of the Long-short Term Memory (LSTM) 
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