The main goal of the project is to provide the user of DeepPavlov Framework with an out-of-box solution for relation extraction. I would consider relation extraction as a multi-label classification task and design a pipeline that could reuse the existing DeepPavlov components (such as NER with incorporated tokenizer, already implemented MT-BERT, and others) as much as possible. I would train several relation extraction models with different parameters (for example, different training data, classifier, classifier inputs, inputs encoding, number of relations to be extracted, etc), add them to the DeepPavlov storage, and give the user an opportunity to load them (and, occasionally, additionally train with his/her own training data) and find the one that would suit his/her data the best way. The user's input would be a text and a config with RE parameters, and relational triples would be the output. Thus, the whole preprocessing, data encoding and model application would happen inside the module, while the overall relation extraction component remains clear and convenient for the user.

Student: Anastasiia Sedova
Mentor: Dmitry Evseev

The TripPy architecture brings transformer models to Goal-oriented Chatbots. While setting new SoTA results on MultiWOZ among others, TripPy also simplifies the previous standard architecture. A better + simpler solution is what we strive for in all fields. This project implements TripPy in DeepPavlov to allow anybody to build fully functional Dialog Bots on top of TripPy leveraging the DeepPavlov ecosystem. Further, the project includes a simple demo in the form of a WeatherBot as well as a more complex chatbot demo built on the new model in DeepPavlov.

Student: Niklas Muennighoff
Mentor: Oleg Serikov