Pith. sign in

REVIEW 1 cited by

ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2002.04793 v2 pith:6ICF4JZY submitted 2020-02-12 cs.CL cs.AI

classification cs.CLcs.AI
keywords dialoguesystemsystemstoolanalysisconvlab-2convlabdiagnose
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We present ConvLab-2, an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models, perform an end-to-end evaluation, and diagnose the weakness of systems. As the successor of ConvLab (Lee et al., 2019b), ConvLab-2 inherits ConvLab's framework but integrates more powerful dialogue models and supports more datasets. Besides, we have developed an analysis tool and an interactive tool to assist researchers in diagnosing dialogue systems. The analysis tool presents rich statistics and summarizes common mistakes from simulated dialogues, which facilitates error analysis and system improvement. The interactive tool provides a user interface that allows developers to diagnose an assembled dialogue system by interacting with the system and modifying the output of each system component.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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

  1. MATRIX: Multi-Agent simulaTion fRamework for safe Interactions and conteXtual clinical conversational evaluation

    cs.AI 2025-08 conditional novelty 5.0 of 10

    MATRIX combines a structured safety taxonomy, an LLM hazard judge, and a patient simulator to benchmark clinical dialogue agents, claiming expert-level hazard detection and revealing weak emergency handling in current LLMs.

Pith tools