This repository contains source code for the TaBERT model, a pre-trained language model for learning joint representations of natural language utterances and (semi-)structured tables for semantic parsing. TaBERT is pre-trained on a massive corpus of 26M Web tables and their associated natural language context, and could be used as a drop-in replacement of a semantic parsers original encoder to compute representations for utterances and table schemas (columns).
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What is the process of selecting relevant rows? #7
The paper proposes that we use N-gram overlap to select rows relevant to the statement/context to make a content snapshot. Is this done during prprocessing the dataset? Doesn't that mean this operation cannont be optimized?
I am a bit confused. Because feeding all rows of a chart into the model requests too much memory, I guess the rows are already selected before this.
It will be much appreciated if you can make this clearer.
Thanks!
The paper proposes that we use N-gram overlap to select rows relevant to the statement/context to make a content snapshot. Is this done during prprocessing the dataset? Doesn't that mean this operation cannont be optimized? I am a bit confused. Because feeding all rows of a chart into the model requests too much memory, I guess the rows are already selected before this. It will be much appreciated if you can make this clearer. Thanks!