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Rasa: Open Source Language Understanding and Dialogue Management

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arxiv 1712.05181 v2 pith:LWXOOXDU submitted 2017-12-14 cs.CL cs.AIcs.LG

Tom Bocklisch , Joey Faulkner , Nick Pawlowski , Alan Nichol This is my paper · ORCID
classification cs.CLcs.AIcs.LG
keywords rasadialoguelanguagemanagementopensoftwaresourceunderstanding
verification ladder T0 review T1 audit T2 compute T3 formal
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We introduce a pair of tools, Rasa NLU and Rasa Core, which are open source python libraries for building conversational software. Their purpose is to make machine-learning based dialogue management and language understanding accessible to non-specialist software developers. In terms of design philosophy, we aim for ease of use, and bootstrapping from minimal (or no) initial training data. Both packages are extensively documented and ship with a comprehensive suite of tests. The code is available at https://github.com/RasaHQ/

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Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Uncertainty as a Planning Signal: Multi-Turn Decision Making for Goal-Oriented Conversation

    cs.CL 2026-04 conditional novelty 6.0 of 10

    Uncertainty-guided MCTS over LLM-proposed ask/commit actions raises goal-oriented dialogue success while cutting turns across four benchmarks and three LLM backbones.

  2. RV4Chatbot: Are Chatbots Allowed to Dream of Electric Sheep?

    cs.AI 2024-11 conditional novelty 5.0 of 10

    RV4Chatbot adds a runtime monitor to intent-based chatbots that flags conversation steps violating interaction protocols, demonstrated on Rasa and Dialogflow.

  3. Training-Free versus Training-Based Intent Classification in LLMs: Accuracy, Robustness, and Failure Modes

    cs.CL 2026-08 conditional novelty 4.0 of 10

    Statistical classifiers built on LLM activation norms and coordinates match or beat trained MLP heads on coarse intent routing and resist camouflage better, while MLPs win on fine-grained subfield distinctions.

  4. RPO-PDT: Demonstrating Role-Play-Based Knowledge Adaptation for Student Support Dialogue (Demonstration System)

    cs.RO 2026-06 unverdicted novelty 4.0 of 10

    RPO-PDT demonstrates a role-play-based, retrieval-grounded system for adaptive, policy-constrained student support dialogue with reverse-roleplay for strategy memory.

  5. Task-Oriented Dialog Systems for the Senegalese Wolof Language

    cs.CL 2024-12 conditional novelty 4.0 of 10

    A Rasa-based Wolof task-oriented dialog system, trained on French MASSIVE data projected through an in-house French-Wolof machine translation system, achieves near-French intent classification but weaker slot filling.

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