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: To further improve the performance, we adopt system combination on the decoding lattice level. By combining systems, we can take advantage of the strength of each model that is optimized for different domains. The best result for vlsp2018 of 4.85% WER is obtained by the combination weights 0.6:0.4 where 0.6 is given to the general language model and 0.4 is given to the conversation one. On the vlsp2019 set, the ratio is change slightly by 0.7:0.3 to deliver the best result of 15.09%.
Question: What is the language model combination technique used in the paper?

SOLUTION: system combination on the decoding lattice level combination weights

PROBLEM: Table TABREF23 shows the Pearson, Spearman and Kendall correlation of Rouge and Sera, with pyramid scores. Interestingly, we observe that many variants of Rouge scores do not have high correlations with human pyramid scores. The lowest F-score correlations are for Rouge-1 and Rouge-L (with INLINEFORM0 =0.454). Weak correlation of Rouge-1 shows that matching unigrams between the candidate summary and gold summaries is not accurate in quantifying the quality of the summary.
Question: What different correlations result when using different variants of ROUGE scores?

SOLUTION: we observe that many variants of Rouge scores do not have high correlations with human pyramid scores

PROBLEM: Given raw audio samples INLINEFORM0 , we apply the encoder network INLINEFORM1 which we parameterize as a five-layer convolutional network similar to BIBREF15 . Next, we apply the context network INLINEFORM0 to the output of the encoder network to mix multiple latent representations INLINEFORM1 into a single contextualized tensor INLINEFORM2 for a receptive field size INLINEFORM3 . The context network has seven layers and each layer has kernel size three and stride one.
Question: How many convolutional layers does their model have?

SOLUTION:
wav2vec has 12 convolutional layers