• Corpus ID: 237532209

Alquist 4.0: Towards Social Intelligence Using Generative Models and Dialogue Personalization

@article{Konrd2021Alquist4T,
  title={Alquist 4.0: Towards Social Intelligence Using Generative Models and Dialogue Personalization},
  author={Jakub Konr{\'a}d and Jan Pichl and Petro Marek and Petr Lorenc and Van Duy Ta and Ondrej Kobza and Lenka H{\'y}lov{\'a} and Jan Sediv{\'y}},
  journal={ArXiv},
  year={2021},
  volume={abs/2109.07968}
}
The open domain-dialogue system Alquist has a goal to conduct a coherent and engaging conversation that can be considered as one of the benchmarks of social intelligence. The fourth version of the system, developed within the Alexa Prize Socialbot Grand Challenge 4, brings two main innovations. The first addresses coherence, and the second addresses the engagingness of the conversation. For innovations regarding coherence, we propose a novel hybrid approach combining hand-designed responses and… 
Flowstorm: Open-Source Platform with Hybrid Dialogue Architecture
TLDR
A novel dialogue architecture is proposed that uses a combination of tree structures with generative models suitable for specific dialogue scenarios and is presented in Flowstorm, a conversational AI platform suitable for creating, running, and analyzing conversational applications.
Improving Bot Response Contradiction Detection via Utterance Rewriting
TLDR
This work curated a new dataset for utterance rewriting and built a rewriting model on it that can produce satisfactory rewrites to make bot utterances more complete and improves contradiction detection performance, e.g., the AUPR and joint accuracy scores increase.
Stylistic Response Generation by Controlling Personality Traits and Intent
TLDR
Automatic annotation schemes are employed to enrich the corpora with noisy estimates of personality and intent annotations, and the impact of using such features as control codes for response generation using automatic evaluation metrics, ablation studies and human judgement is assessed.
Metric Learning and Adaptive Boundary for Out-of-Domain Detection
TLDR
This work has designed an OOD detection algorithm independent of OOD data that outperforms a wide range of current state-of-the-art algorithms on publicly available datasets and is based on a simple butcient approach of combining metric learning with adaptive decision boundary.
Let’s Chat: Understanding User Expectations in Socialbot Interactions
TLDR
It is found that because socialbots are a new genre of HCI, they are still negotiating norms to guide interactions in this setting, which has important implications for guiding future work in the development of conversational agents.

References

SHOWING 1-10 OF 30 REFERENCES
Neural Generation Meets Real People: Towards Emotionally Engaging Mixed-Initiative Conversations
TLDR
This work presents Chirpy Cardinal, an open-domain dialogue agent, as a research platform for the 2019 Alexa Prize competition, capable of talking knowledgeably about a wide variety of topics, as well as chatting empathetically about ordinary life.
Alquist 3.0: Alexa Prize Bot Using Conversational Knowledge Graph
TLDR
The third version of the open-domain dialogue system Alquist developed within the Alexa Prize 2020 competition is designed to conduct coherent and engaging conversations on popular topics by leveraging an innovative approach based on a conversational knowledge graph and adjacency pairs.
Further Advances in Open Domain Dialog Systems in the Third Alexa Prize Socialbot Grand Challenge
TLDR
This paper outlines the advances developed by the university teams as well as the Alexa Prize team to move closer to the Grand Challenge objective, addressing several key open-ended problems such as conversational speech recognition, open domain natural language understanding, commonsense reasoning, statistical dialog management and dialog evaluation.
Towards Empathetic Open-domain Conversation Models: A New Benchmark and Dataset
TLDR
This work proposes a new benchmark for empathetic dialogue generation and EmpatheticDialogues, a novel dataset of 25k conversations grounded in emotional situations, and presents empirical comparisons of dialogue model adaptations forEmpathetic responding, leveraging existing models or datasets without requiring lengthy re-training of the full model.
DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation
TLDR
It is shown that conversational systems that leverage DialoGPT generate more relevant, contentful and context-consistent responses than strong baseline systems.
Alquist: The Alexa Prize Socialbot
TLDR
A hybrid system combining several machine learning and rule based approaches is presented, designed to conduct a coherent and engaging conversation on popular topics for the Amazon Echo line of products.
Alquist 2.0: Alexa Prize Socialbot Based on Sub-Dialogue Models
TLDR
The second version of the dialogue system named Alquist competing in Amazon Alexa Prize 2018 is presented, leveraging ontology-based topic structure called topic nodes, which consists of several sub-dialogues which can be triggered according to the topic hierarchy or a user intent.
Contextual Out-of-domain Utterance Handling with Counterfeit Data Augmentation
  • Sungjin Lee, Igor Shalyminov
  • Computer Science
    ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2019
TLDR
The goal of this paper is to propose a novel OOD detection method that does not require OOD data by utilizing counterfeit OOD turns in the context of a dialog by outperforms state-of-the-art dialog models equipped with a conventional Ood detection mechanism by a large margin in the presence of OOD utterances.
Topical-Chat: Towards Knowledge-Grounded Open-Domain Conversations
TLDR
Topical-Chat is introduced, a knowledge-grounded humanhuman conversation dataset where the underlying knowledge spans 8 broad topics and conversation partners don’t have explicitly defined roles, to help further research in opendomain conversational AI.
Dialogue Response Ranking Training with Large-Scale Human Feedback Data
TLDR
This work trained DialogRPT, a set of GPT-2 based models on 133M pairs of human feedback data and the resulting ranker outperformed several baselines, and outperforms the conventional dialog perplexity baseline with a large margin on predicting Reddit feedback.
...
...