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.
One example: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese Short Text Summarization Dataset (LCSTS) is constructed by BIBREF1 . The dataset consists of more than 2.4 million text-summary pairs in total, constructed from a famous Chinese social media microblogging service Weibo.
Question: Are results reported only for English data?
Solution is here: No
Explanation: Based on the context, the dataset is constructed from a famous Chinese social media microblogging service Weibo.

Now, solve this: Analysis of challenges from UTD
Our system relies on the pseudotext produced by ZRTools (the only freely available UTD system we are aware of), which presents several challenges for MT. Assigning wrong words to a cluster
Since UTD is unsupervised, the discovered clusters are noisy. Splitting words across different clusters
Although most UTD matches are across speakers, recall of cross-speaker matches is lower than for same-speaker matches. As a result, the same word from different speakers often appears in multiple clusters, preventing the model from learning good translations. UTD is sparse, giving low coverage We found that the patterns discovered by ZRTools match only 28% of the audio. This low coverage reduces training data size, affects alignment quality, and adversely affects translation, which is only possible when pseudoterms are present.
Question: what challenges are identified?
Solution:
Assigning wrong words to a cluster Splitting words across different clusters sparse, giving low coverage