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]: 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?
[A]: system combination on the decoding lattice level combination weights

[Q]: More than 2,100 texts were paired with 15 questions each, resulting in a total number of approx. 32,000 annotated questions. For 13% of the questions, the workers did not agree on one of the 4 categories with a 3 out of 5 majority, so we did not include these questions in our dataset.

The distribution of category labels on the remaining 87% is shown in Table TABREF10 . 14,074 (52%) questions could be answered. Out of the answerable questions, 10,160 could be answered from the text directly (text-based) and 3,914 questions required the use of commonsense knowledge (script-based). After removing 135 questions during the validation, the final dataset comprises 13,939 questions, 3,827 of which require commonsense knowledge (i.e. 27.4%). This ratio was manually verified based on a random sample of questions.
Question: what dataset statistics are provided?
[A]: More than 2,100 texts were paired with 15 questions each, resulting in a total number of approx. 32,000 annotated questions. 13% of the questions are not answerable. Out of the answerable questions, 10,160 could be answered from the text directly (text-based) and 3,914 questions required the use of commonsense knowledge (script-based). The final dataset comprises 13,939 questions, 3,827 of which require commonsense knowledge (i.e. 27.4%).

[Q]: The ratio of correct 'translations' (matches) was used as an evaluation measure.
Question: What evaluation metric do they use?
[A]:
Accuracy