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.

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?
system combination on the decoding lattice level combination weights

We collect data by using the Twitter API for saved data, available for public use. For our experiments we collect 3200 tweets filtered by keywords like "fire", "earthquake", "theft", "robbery", "drunk driving", "drunk driving accident" etc.
Question: Do the tweets come from any individual?
Yes

In this work, we propose to augment the integer linear programming (ILP)-based summarization framework with a low-rank approximation of the co-occurrence matrix, and further evaluate the approach on a broad range of datasets exhibiting high lexical diversity.
Question: What do they constrain using integer linear programming?
low-rank approximation of the co-occurrence matrix