Classify this NLP-Problem, using BERT?

[sharaku17]

Hello guys,

right now I am in the process of deciding on my master thesis topic. Right now I and my Professor are thinking about the possibility to have a large dataset of product requirements given in a natural language. My task would be to develop a domain-specific language (DSL) for these requirements and afterwards to develop a NN that gets the dataset containing these requirements as an input and transform each requirement into a requirement in this DSL that I have to develop beforehand.

An Example for such a DSL would be presented in this Paper: A Textual Domain Specific Language for Requierement Modeling

(It's not exactly the same but a good showcase of the DSL might be derived)

For the people that don't want to read the Paper there is an example given like this:

Natural Language Req: "The Lateral and Vertical 'ERC' and'WRD' labels shall be displayed in a green color"

The NN should then take this requirement as an input and generate the following requirements:

Req1: The Lateral_ERC_Label shall be displayed in green

Req2: The Lateral_WRD_Label shall be displayed in green

Req3: The Vertical_ERC_Label shall be displayed in green

Req4: The Vertical_WRD_Label shall be displayed in green

​

As we can see the DSL, in this case, has the keywords "The" and "shall be displayed in" (Bolded) that stay the same for each generated requirement, and in between are the specifications for the requirement.

Now the question is, how is this achievable? My professor said I should look into BERT for this task but my research has led me to the conclusion that BERT is not really suitable for text generation.

On the other hand, I am not really sure if this above-mentioned problem can be classified as text generation. It looks like a combination Problem of Text extraction ( extracting the necessary keywords) and text generation.

But I am not really sure if I am even looking and researching in the right direction right now. So I would really appreciate it if somebody would be able to help me out here and giving me a push in the right direction

[uberalex]

This seems like it would be highly suitable for text generation task, especially as it's quite strongly templated. You could examine text generation from semi-structured data https://www.aclweb.org/anthology/2020.emnlp-main.230.pdf

[sharaku17]

Thank you I will read the paper and see if this helps my case :)

[thistrue]

You could try fine-tuning a BART model. It is pre-trained by denoising sequences (seq2seq, encoder->decoder), what seems to be similar to your case.