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.
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Question: Results in Table TABREF13 show ten probes. Again, we see the performance of baseline ELMo-transformer and mSynC are similar, with mSynC doing slightly worse on 7 out of 9 tasks. 
 Question: For how many probe tasks the shallow-syntax-aware contextual embedding perform better than ELMo’s embedding?

Answer: performance of baseline ELMo-transformer and mSynC are similar, with mSynC doing slightly worse on 7 out of 9 tasks


Question: We use three datasets for our experiments: IWSLT14 German-English, Turkish-English, and WMT14 English-German. 
 Question: what language pairs are explored?

Answer: German-English Turkish-English English-German


Question: The greatest challenges with the competition arise directly from the potential for ongoing mixed-initiative multi-turn dialogues, which do not follow a particular plan or pursue a particular fixed information need.  This paper describes some of the lessons we learned building SlugBot for the 2017 Alexa Prize, particularly focusing on the challenges of integrating content found via search with content from structured data in order to carry on an ongoing, coherent, open-domain, mixed-initiative conversation More challenging is that at each system turn, there are a large number of conversational moves that are possible.  Finally, most other domains do not have such high quality structured data available, leaving us to develop or try to rely on more general models of discourse coherence.  Search cannot be used effectively here without constructing an appropriate query, or knowing in advance where plot information might be available. In a real-time system, live search may not be able to achieve the required speed and efficiency, so preprocessing or caching of relevant information may be necessary.  
 Question: Why mixed initiative multi-turn dialogs are the greatest challenge in building open-domain conversational agents?

Answer:
do not follow a particular plan or pursue a particular fixed information need  integrating content found via search with content from structured data at each system turn, there are a large number of conversational moves that are possible most other domains do not have such high quality structured data available live search may not be able to achieve the required speed and efficiency