TASK DEFINITION: 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.
PROBLEM: We chose SVMhmm BIBREF111 implementation of Structural Support Vector Machines for sequence labeling. SVM implementation of   
 Question: Which machine learning methods are used in experiments?

SOLUTION: Structural Support Vector Machine

PROBLEM: While the performances of a purely content-based model naturally stays stable, the performance of the other systems decrease notably – they perform worse than the content-based model. 
 Question: How do they demonstrate the robustness of their results?

SOLUTION: performances of a purely content-based model naturally stays stable

PROBLEM: The best performing systems being for character unit the RNN-transducer with additional attention module, achieving 7.8% in terms of CER and 17.6% on WER. For subword units, classic RNN-transducer, RNN-transducer with attention and joint CTC-attention show comparable performance on subword error rate and WER, with the first one being slightly better on WER ($17.4\%$) and the last one having a lower error rate on subword ($14.5\%$). 
 Question: Which model have the smallest Character Error Rate and which have the smallest Word Error Rate?

SOLUTION:
character unit the RNN-transducer with additional attention module For subword units, classic RNN-transducer, RNN-transducer with attention and joint CTC-attention show comparable performance