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.

Example Input: We evaluated the proposed model using the publicly available LibriSpeech ASR corpus BIBREF23. The LibriSpeech dataset consists of 970 hours of audio data with corresponding text transcripts (around 10M word tokens) and an additional 800M word token text only dataset.
Question: How big is LibriSpeech dataset?
Example Output: 970 hours of audio data with corresponding text transcripts (around 10M word tokens) and an additional 800M word token text only dataset

Example Input: SIGHAN Bakeoff defines two types of evaluation settings, closed test limits all the data for learning should not be beyond the given training set, while open test does not take this limitation BIBREF21.
Question: What is meant by closed test setting?
Example Output: closed test limits all the data for learning should not be beyond the given training set, while open test does not take this limitation

Example Input: FastTextBIBREF4: It uses bag of words and bag of n-grams as features for text classification, capturing partial information about the local word order efficiently. BiLSTM: Unlike feedforward neural networks, recurrent neural networks like BiLSTMs use memory based on history information to learn long-distance features and then predict the output. We use a two-layer BiLSTM architecture with GloVe word embeddings as a strong RNN baseline. BERT BIBREF5: It is a contextualized word representation model that uses bidirectional transformers, pretrained on a large $3.3B$ word corpus. We use the $BERT_{large}$ model finetuned on the training dataset.
Question: What is the baseline for the experiments?
Example Output:
FastText BiLSTM BERT