DREAM Technical Report for the Alexa Prize 4

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DREAM Technical Report for the Alexa Prize 4

Alexa Prize Proceedings, 2021.

Baymurzina D., Kuznetsov D., Evseev D., Karpov D., Sagirova A., Peganov A., Ignatov F., Ermakova E., Cherniavskii D., Kumeyko S., Oleg Serikov, Kuratov Y., Ostyakova L., Kornev D., Burtsev M.

In this report, we present the DREAM 2 Socialbot design and share scientific
and technology contributions made towards developing a fluent and meaningful
socialbot for Alexa Prize 4. Building on top of the last year’s solution we added a
rich plethora of the script-driven skills created with the help of the novel Dialogue
Flow Framework. To lay down the foundation for the discourse-driven dialogue
strategy management we introduced tag-based Response Selector and Speech
Functions Classifier. We also began working on User and Bot Persona Knowledge
Graphs as well as incorporated our work on World Knowledge Graph alongside
with Entity Linking. The final version of DREAM 2 Socialbot is still a hybrid
system that combines rule-based, deep learning, and knowledge based driven
components, but it moves closer to a goal-aware system that can recognize users’
and own goals and drive the dialogue strategically

Language: English

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Keywords: dialogue systems

DREAM Technical Report for the Alexa Prize 4

Personalizing chatbot communication with associative memory

Soloshenko K., Shatalina A., Marina S. et al., , in: Proceedings of the 9th Student Research Workshop associated with the International Conference Recent Advances in Natural Language Processing.: Shumen: INCOMA Ltd, 2025. P. 62–69.

In our research paper we present the approach that is aimed at effectively expanding the context through integrating a database of associative memory into the pipeline. In order to improve long-term memory and personalization we have utilized methods close to Retrieval-Augmented Generation (RAG). Our method uses a multi-agent pipeline with a cold-start agent for initial ...

Added: May 12, 2026

3MDBench: Medical Multimodal Multi-agent Dialogue Benchmark

Sviridov I., Miftakhova A., Tereshchenko A. et al., , in: Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP).: Association for Computational Linguistics, 2025. Ch. 1353 P. 26625–26665.

Though Large Vision-Language Models (LVLMs) are being actively explored in medicine, their ability to conduct complex real-world telemedicine consultations combining accurate diagnosis with professional dialogue remains underexplored. This paper presents 3MDBench (Medical Multimodal Multi-agent Dialogue Benchmark), an open-source framework for simulating and evaluating LVLM-driven telemedical consultations. 3MDBench simulates patient variability through temperament-based Patient Agent and evaluates diagnostic accuracy and dialogue quality ...

Added: November 16, 2025

Диалоговая система для сопровождения человека, страдающего реактивной депрессией. Лингвистический аспект

Сиверцев Е. Ю., Фирсанова В. И., Глобальный научный потенциал 2024 Т. 155 № 2 С. 226–231

The purpose of the article is to give recommendations for creating a dialogue system that can be connected to any electronic device (personal computer, smartphone) and should always be at the disposal of a person suffering from reactive depression. The method of distributive semantics is used: searching for the meaning of a word by its ...

Added: July 12, 2025

Linguistic Annotation Generation with ChatGPT: a Synthetic Dataset of Speech Functions for Discourse Annotation of Casual Conversations

Ostyakova Lidiia, Petukhova K., Smilga V. et al., , in: Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference “Dialogue 2023”Т. 22.: М.: [б.и.], 2023.

This paper is devoted to examining the hierarchical and multilayered taxonomy of Speech Functions, encompassing pragmatics, turn-taking, feedback, and topic switching in open-domain conversations. To evaluate the distinctiveness of closely related pragmatic classes, we conducted comparative analyses involving both expert annotators and crowdsourcing workers. We then carried out classification experiments on a manually annotated dataset ...

Added: May 24, 2024

Socialbot Grand Challenge 3

[б.и.], 2019.

Building a dialogue system able to talk fluently and meaningfully in an open domain conversation is one of the foundational challenges in the field of AI. Recent progress in NLP driven by the application of the deep neural networks and large language models opened new possibilities to solve many hard problems of the conversational AI. Alexa Prize Socialbot Grand ...

Added: September 16, 2021

Проблемы обработки естественного языка в диалоговых системах

Klyshinskiy E., Жеребцова Ю., Чижик А., Системный администратор 2019 № 10 С. 82–91

Nowadays, a field of dialogue systems and conversational agents is one of the rapidly growing research areas in artificial intelligence applications. Business and industry are showing increasing interest in implementing intelligent conversational agents into their products. Many recent studies has tended to focus on possibility of developing task-oriented systems which are able to have long ...

Added: October 26, 2019

Artificial Intelligence and Natural Language, 7th International Conference, AINL 2018, St. Petersburg, Russia, October 17–19, 2018, Proceedings

Switzerland: Springer, 2018.

This book constitutes the refereed proceedings of the 7th Conference on Artificial Intelligence and Natural Language, AINL 2018, held in St. Petersburg, Russia, in October 2018. The 19 revised full papers were carefully reviewed and selected from 56 submissions and cover a wide range of topics, including morphology and word-level semantics, sentence and discourse representations, corpus ...

Added: August 30, 2018