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.

[Q]: In this work, we introduce a new logical inference engine called MonaLog, which is based on natural logic and work on monotonicity stemming from vanBenthemEssays86. Since our logic operates over surface forms, it is straightforward to hybridize our models. We investigate using MonaLog in combination with the language model BERT BIBREF20, including for compositional data augmentation, i.e, re-generating entailed versions of examples in our training sets. We perform two experiments to test MonaLog. We first use MonaLog to solve the problems in a commonly used natural language inference dataset, SICK BIBREF1, comparing our results with previous systems. Second, we test the quality of the data generated by MonaLog. To do this, we generate more training data (sentence pairs) from the SICK training data using our system, and performe fine-tuning on BERT BIBREF20, a language model based on the transformer architecture BIBREF23, with the expanded dataset.
Question: How do they combine MonaLog with BERT?
[A]: They use Monalog for data-augmentation to fine-tune BERT on this task

[Q]: An acoustic word embedding is a function that takes as input a speech segment corresponding to a word, INLINEFORM0 , where each INLINEFORM1 is a vector of frame-level acoustic features, and outputs a fixed-dimensional vector representing the segment, INLINEFORM2 .
Question: How do they represent input features of their model to train embeddings?
[A]: a vector of frame-level acoustic features

[Q]: For answer retrieval, a dataset is created by INLINEFORM4 , which gives INLINEFORM5 accuracy and INLINEFORM6 coverage, respectively.
Question: Do they employ their indexing-based method to create a sample of a QA Wikipedia dataset?
[A]:
Yes