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.

We discuss two core models for addressing sequence labeling problems and describe, for each, training them in a single-model multilingual setting: (1) the Meta-LSTM BIBREF0 , an extremely strong baseline for our tasks, and (2) a multilingual BERT-based model BIBREF1 .
What is the multilingual baseline?

Table TABREF24 shows that while the acceleration of trading decoder layers for encoding layers in training is small, in decoding is significant. Specifically, the Transformer with 10 encoder layers and 2 decoder layers is $2.32$ times as fast as the 6-layer Transformer while achieving a slightly higher BLEU.
How much is decoding speed increased by increasing encoder and decreasing decoder depth?

n-gram: We consider 3-gram and 4-gram conditional language model baseline with interpolation. We use random grid search for the best coefficients for the interpolated model.

Convolution: We use the fconv architecture BIBREF24 and default hyperparameters from the fairseq BIBREF25 framework. We train the network with ADAM optimizer BIBREF26 with learning rate of 0.25 and dropout probability set to 0.2.

LSTM: We consider LSTM models BIBREF27 with and without attention BIBREF28 and use the tensor2tensor BIBREF29 framework for the LSTM baselines. We use a two-layer LSTM network for both the encoder and the decoder with 128 dimensional hidden vectors.

Transformer: As with LSTMs, we use the tensor2tensor framework for the Transformer model. Our Transformer BIBREF21 model uses 256 dimensions for both input embedding and hidden state, 2 layers and 4 attention heads. For both LSTMs and Transformer, we train the model with ADAM optimizer ($eta _{1} = 0.85$, $eta _{2} = 0.997$) and dropout probability set to 0.2.

GPT-2: Apart from supervised seq2seq models, we also include results from pre-trained GPT-2 BIBREF30 containing 117M parameters.
What baseline models are offered?