In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

Input: Consider Input: To evaluate the proposed BiLSTM model with attention (BiLSTM+att), it is compared with three of its own variants: a BiLSTM without attention (BiLSTM) as well as a single forward-LSTM layer with attention (LSTM+att) and without attention (LSTM). Additional baselines are defined by BIBREF32 who already proposed an LSTM-based architecture that only uses non-temporal features, and the SVM-based estimation model as originally used for reward estimation by BIBREF24.

Output: What model do they use a baseline to estimate satisfaction?


Input: Consider Input: convertible.pl: implementing DCG rules for 1st and 3rd steps in the three-steps conversion, as well as other rules including lexicon.

Output: What DCGs are used?


Input: Consider Input: For the benchmarks, we selected five systems. We picked first the langid.py library which is frequently used to compare systems in the literature. Since our work is in neural-network LID, we selected two neural network systems from the literature, specifically the encoder-decoder EquiLID system of BIBREF6 and the GRU neural network LanideNN system of BIBREF10. Finally, we included CLD2 and CLD3, two implementations of the Naïve Bayes LID software used by Google in their Chrome web browser BIBREF4, BIBREF0, BIBREF8 and sometimes used as a comparison system in the LID literature BIBREF7, BIBREF6, BIBREF8, BIBREF2, BIBREF10.


Output: Which existing language ID systems are tested?