A Multilingual Neural RDF Verbalizer about gsoc

[aditya-malte]

I find this problem interesting. We could improve the state-of-the-art by implementing a Transformer architecture(A. Vaswani et. al.) for these very same papers.

[SilentFlame]

This problem statement really interests me, I'll look around with the papers mentioned and get back with queries if stuck in between.

[DwaraknathT]

This problem is similar to what I am working on right now, and it's very interesting. I have created a repo and will upload the summaries of papers that are mentioned here along with my own improvement ideas, please go through them and give feedback. Also, porting the NeuralREG code to pytorch might be really helpful and pytext is a boon for data preprocessing.
https://github.com/DwaraknathT/NLG-

[DiegoMoussallem]

Hi @ovshake, "Multilingual" means to be able to generate a natural language sentence in multiple languages from a given RDF input <s,p,o>. It is not only about the referring expression generation, but it must also include a complete verbalization, i.e., with articles, verbs and etc.

[ovshake]

I have messaged you on skype.

[ovshake]

@DiegoMoussallem I have shared my proposal on the GSoC platform. Do review it at your convenience.

[DiegoMoussallem]

@DwaraknathT I have been following your summaries, let me know if you need some help. We can have a talk as well. Looking forward to next summaries.

[DiegoMoussallem]

@SilentFlame How about you? How is it going with the papers?

[DwaraknathT]

@DiegoMoussallem Thank you, I appreciate any comments or suggestions you have for me. Right now, I'm trying to reimplement NeuralREG code but with transformers, to see how much gains we might be abe to get. The fundamental algorithm is same, but it was just a hunch to understand the code properly. May I contact you on slack for further discussions ?

[DiegoMoussallem]

of course, you may contact me. I got your point about checking with the Transformer, but it might not be so necessary at the moment.

[DiegoMoussallem]

Hi @aditya-malte It sounds good. Could you send me a message with the details?

[ovshake]

Hi @DiegoMoussallem ! I feel this problem statement well aligns with my current research interests and would love to work on it. I agree with @aditya-malte , transformer architecture should show improvement, but I feel the extra computational complexity introduced by that architecture should be compensated by introducing efficient attention models such as lightweight and dynamic convolutional attention mentioned here. Quasi-RNNs should be swapped with vanilla-LSTMs. This will allow us to generate even longer and descriptive sentences. I would love to work on this if this is not already taken.

[DiegoMoussallem]

@ovshake Thanks for being interested in this project. Your plan sounds good, I am looking forward to seeing your proposal. Feel free to contact me.

[ovshake]

@DiegoMoussallem Does "Multilingual" mean the model should be able to generate the referring expression in a language other than the language in which the RDF is given or the model should be able to handle any language as the input RDF (other than the language it is trained on) and should give the RF in the same language?

[ovshake]

Hi @DiegoMoussallem, I have some questions regarding my proposal, which platform should I use reach out to you that is most convenient to you?

[DiegoMoussallem]

Hey @ovshake feel free to contact me on skype diegomoussallem