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.

[Q]: The current release of Katecheo uses a Bi-Directional Attention Flow, or BiDAF, model for reading comprehension BIBREF6 . Future releases of Katecheo will include the ability to swap out the reading comprehension model for newer architectures based on, e.g., BERT BIBREF8 or XLNet BIBREF9 or custom trained models.

Architecture and Configuration
[A]: what pretrained models were used?

[Q]: Evaluating FastText, GloVe and word2vec, we show that compared to other word representation learning algorithms, the FastText performs best.
[A]: What do you use to calculate word/sub-word embeddings

[Q]: Vanilla ST baseline: The vanilla ST BIBREF9 has only a speech encoder and a decoder. It is trained from scratch on the ST-TED corpus.

Pre-training baselines: We conduct three pre-training baseline experiments: 1) encoder pre-training, in which the ST encoder is initialized from an ASR model; 2) decoder pre-training, in which the ST decoder is initialized from an MT model; and 3) encoder-decoder pre-training, where both the encoder and decoder are pre-trained. The ASR model has the same architecture with vanilla ST model, trained on the mixture of ST-TED and TED-LIUM2 corpus. The MT model has a text encoder and decoder with the same architecture of which in TCEN. It is first trained on WMT data (out-of-domain) and then fine-tuned on in-domain data.

Multi-task baselines: We also conduct three multi-task baseline experiments including one-to-many setting, many-to-one setting, and many-to-many setting. In the first two settings, we train the model with $lpha _{st}=0.75$ while $lpha _{asr}=0.25$ or $lpha _{mt}=0.25$. For many-to-many setting, we use $lpha _{st}=0.6, lpha _{asr}=0.2$ and $lpha _{mt}=0.2$.. For MT task, we use only in-domain data.

Many-to-many+pre-training: We train a many-to-many multi-task model where the encoders and decoders are derived from pre-trained ASR and MT models. Triangle+pre-train: BIBREF18 DBLP:conf/naacl/AnastasopoulosC18 proposed a triangle multi-task strategy for speech translation. Their model solves the subnet waste issue by concatenating an ST decoder to an ASR encoder-decoder model. Notably, their ST decoder can consume representations from the speech encoder as well as the ASR decoder. For a fair comparison, the speech encoder and the ASR decoder are initialized from the pre-trained ASR model. The Triangle model is fine-tuned under their multi-task manner.
[A]:
What are the baselines?