We propose an application of evolutionary algorithm to Neural Architecture Search for image and text classification tasks on PyTorch. One of the main issues of evolutionary search is high resource consumption, so one of our main priority is optimisation of the model evaluation.

Responses can be more meaningful compared to chit-chat generated responses thanks to usage of facts coming from a knowledge base like Wikidata. The system consists of three components:

1. extraction of triplets from Wikidata for entities from the user's utterance;
2. triplets ranking (choosing the triplet which is the most appropriate to use for generation of the response utterance);
3. generation of the response utterance.

During decoding in Transformer, we simultaneously predict the next token and its type: memory or sequence. Memory tokens provide space to store global representations of the input context.

Disambiguation of candidate entities for extracted entities is performed using

1. prior probabilities of mention and entity correspondence;
2. context (the sentence in the text which contains the mentioned entity) and description of the entity in Wikidata;
3. global disambiguation (joint disambiguation of all entities in the text using connections of the entities in Wikidata knowledge graph).

We detect errors in dates and causal relationship. We collected databases of dates and causal relationship of historic events. For date checking we extracted tokens in the text which refer to historic events using syntactic parser, then linked it to the event in database using TF-IDF and matched the date in text with the date in the database.