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: Although techniques for constructing this knowledge base are outside the scope of this paper, we briefly mention them. Tables were constructed using a mixture of manual and semi-automatic techniques 
 Question: How is the semi-structured knowledge base created?

SOLUTION: using a mixture of manual and semi-automatic techniques

PROBLEM: In this section, we describe the experimental study. We fit the sefe model on three datasets and compare it against the efe BIBREF10 . Our quantitative results show that sharing the context vectors provides better results, and that amortization and hierarchical structure give further improvements. Data. We apply the sefe on three datasets: ArXiv papers, U.S. Senate speeches, and purchases on supermarket grocery shopping data. We describe these datasets below, and we provide a summary of the datasets in Table TABREF17 . ArXiv papers: This dataset contains the abstracts of papers published on the ArXiv under the 19 different tags between April 2007 and June 2015. We treat each tag as a group and fit sefe with the goal of uncovering which words have the strongest shift in usage. We split the abstracts into training, validation, and test sets, with proportions of INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 , respectively.

Senate speeches: This dataset contains U.S. Senate speeches from 1994 to mid 2009. In contrast to the ArXiv collection, it is a transcript of spoken language. We group the data into state of origin of the speaker and his or her party affiliation. Only affiliations with the Republican and Democratic Party are considered. As a result, there are 83 groups (Republicans from Alabama, Democrats from Alabama, Republicans from Arkansas, etc.). Some of the state/party combinations are not available in the data, as some of the 50 states have only had Senators with the same party affiliation. We split the speeches into training ( INLINEFORM0 ), validation ( INLINEFORM1 ), and testing ( INLINEFORM2 ).

Grocery shopping data: This dataset contains the purchases of INLINEFORM0 customers. The data covers a period of 97 weeks. After removing low-frequency items, the data contains INLINEFORM1 unique items at the 1.10upc (Universal Product Code) level. We split the data into a training, test, and validation sets, with proportions of INLINEFORM2 , INLINEFORM3 , and INLINEFORM4 , respectively. The training data contains INLINEFORM5 shopping trips and INLINEFORM6 purchases in total. For the text corpora, we fix the vocabulary to the 15k most frequent terms and remove all words that are not in the vocabulary. Following BIBREF2 , we additionally remove each word with probability INLINEFORM0 , where INLINEFORM1 is the word frequency. This downsamples especially the frequent words and speeds up training. (Sizes reported in Table TABREF17 are the number of words remaining after preprocessing.) Models. Our goal is to fit the sefe model on these datasets. For the text data, we use the Bernoulli distribution as the conditional exponential family, while for the shopping data we use the Poisson distribution, which is more appropriate for count data. On each dataset, we compare four approaches based on sefe with two efe BIBREF10 baselines. All are fit using sgd BIBREF34 . In particular, we compare the following methods: 
 Question: What experiments are used to demonstrate the benefits of this approach?

SOLUTION:
On each dataset, we compare four approaches based on sefe with two efe BIBREF10 baselines. All are fit using sgd BIBREF34 . In particular, we compare the following methods: