Tartan: A retrieval-based socialbot powered by a dynamic finite-state machine architecture

By George Larionov, Zachary Kaden, Hima Varsha Dureddy, G. B. Kalejaiye, Mihir Kale, Srividya Pranavi Potharaju, Ankit Shah, Alexander I. Rudnicky. 2018

17 citations recorded by the source corpus.

This paper describes the Tartan conversational agent built for the 2018 Alexa Prize Competition. Tartan is a non-goal-oriented socialbot focused around providing users with an engaging and fluent casual conversation. Tartan's key features include an emphasis on structured conversation based on flexible finite-state models and an approach focused on understanding and using conversational acts. To provide engaging conversations, Tartan blends script-like yet dynamic responses with data-based generative and retrieval models. Unique to Tartan is that our dialog manager is modeled as a dynamic Finite State Machine. To our knowledge, no other conversational agent implementation has followed this specific structure.

Subfields: Artificial Intelligence, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Natural Language Processing

Citations in the corpus

Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling (2021, background): “Other approaches include Larionov et al. (2018), who train a classiﬁer to detect controversial content based on Reddit posts that had been ﬂagged as such, and Cercas Curry et al. (2018), who train a support vector machine (SVM) to detect abusive input directed at their social chatbot.”
Guiding the Release of Safer E2E Conversational AI through Value Sensitive Design (2022, background): “…systems often relies on identification of keywords (Ram et al., 2017; Cercas Curry et al., 2018; Fulda et al., 2018; Khatri et al., 2018; Paran-jape et al., 2020), or uses human labels such as flag-ging of a post to train classifiers (Larionov et al., 2018; Cercas Curry et al., 2018).”
Advancing the State of the Art in Open Domain Dialog Systems through the Alexa Prize (2018, methodology): “Tartan (Larionov et al., 2018) trained a supervised model on Reddit data using the “controversial” score given for each user message as their signal.”
SafetyKit: First Aid for Measuring Safety in Open-domain Conversational Systems (2022, background): “…safety sensitive situations In addition to the safety considerations outlined in §2, there are a number of potentially sensitive or controversial topics that may be unsuitable for a system to engage with, such as politics or religion (Hessel and Lee, 2019; Larionov et al., 2018; Xu et al., 2020).”