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.

[EX Q]: We explored different normalization techniques. FBanks with cepstral mean normalization (CMN) perform better than raw FBanks. We found using variance with mean normalization (CMVN) unnecessary for the task. Using deltas and delta-deltas improves model, so we used them in other experiments. Models trained with spectrogram features converge slower and to worse minimum, but the difference when using CMN is not very big compared to FBanks.
Question: What normalization techniques are mentioned?
[EX A]: FBanks with cepstral mean normalization (CMN) variance with mean normalization (CMVN)

[EX Q]: For a word belonging to any one of the word-groups representing these concepts, the modified cost term favors an increase for the value of this word's embedding vector dimension corresponding to the concept that the particular word belongs to.
Question: Along which dimension do the semantically related words take larger values?
[EX A]: dimension corresponding to the concept that the particular word belongs to

[EX Q]: convertible.pl: implementing DCG rules for 1st and 3rd steps in the three-steps conversion, as well as other rules including lexicon.
Question: What DCGs are used?
[EX A]:
Author's own DCG rules are defined from scratch.