TASK DEFINITION: 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.
PROBLEM: Before designing this NLP toolkit, we conducted a survey among engineers and identified a spectrum of three typical personas.

SOLUTION: How do the authors evidence the claim that many engineers find it a big overhead to choose from multiple frameworks, models and optimization techniques?

PROBLEM: For a given transcribed utterance, it is firstly encoded with Byte Pair Encoding (BPE) BIBREF14, a compression algorithm splitting words to fundamental subword units (pairs of bytes or BPs) and reducing the embedded vocabulary size. Then we use a BiLSTM BIBREF15 encoder and the output state of the BiLSTM is regarded as a vector representation for this utterance. Finally, a fully connected Feed-forward Neural Network (FNN) followed by a softmax layer, labeled as a multilayer perceptron (MLP) module, is used to perform the domain/intent classification task based on the vector. We name it Oracle simply because we assume that hypotheses are noisy versions of transcription.

SOLUTION: Which ASR system(s) is used in this work?

PROBLEM: In this section we report on the experiments performed using the system and data described above. First we will present the English results for the three ABSA editions as well as a comparison with previous work. After that we will do the same for 5 additional languages included in the ABSA 2016 edition: Dutch, French, Russian, Spanish and Turkish. The local and clustering features, as described in Section SECREF11 , are the same for every language and evaluation setting. The only change is the clustering lexicons used for the different languages. As stated in section SECREF11 , the best cluster combination is chosen via 5-fold cross validation (CV) on the training data. We first try every permutation with the Clark and Word2vec clusters. Once the best combination is obtained, we then try with the Brown clusters obtaining thus the final model for each language and dataset.

SOLUTION:
Which six languages are experimented with?