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Chatbot evaluation is really hard. There is no standard, and this is our attempt to at least address small parts of this issue.

Right now we using ParlAi as our framework for data as well as experiments. We used OpenMNT-py for training models. All of our checkpoints will be made publicly available including all configurations. See this link for checkpoints from the paper.

Submit your model! Please take a look our submission page.

Amazon Mechanical Turk is not free... we have received initial funding from Joao Sedoc's MSR Dissertation Grant. Thank you Microsoft!

Please find our paper here.

What does ChatEval solve?

  1. Shared and publicly available model code and checkpoints.
  2. Standard evaluation datasets.
  3. Standard human annotator framework (currently using Amazon Mechanical Turk).
  4. Model comparisons of the performance of Model A vs Model B. Both a summary and all data are available.

chateval's Projects

application icon application

A platform for the warehousing and evaluation of neural open domain chatbot models.

archive icon archive

Public evaluation tool for non task driven neural open domain chatbots

autolabel icon autolabel

Label, clean and enrich text datasets with LLMs.

bartscore icon bartscore

BARTScore: Evaluating Generated Text as Text Generation

begin-dataset icon begin-dataset

A benchmark dataset for evaluating dialog system and natural language generation metrics.

botsim icon botsim

BotSIM - a data-efficient end-to-end Bot SIMulation toolkit for evaluation, diagnosis, and improvement of commercial chatbots

conture icon conture

ConTurE is a human-chatbot dataset that contains turn level annotations to assess the quality of chatbot responses.

d-score icon d-score

D-score Framework For Open-domain Automatic Dialogue Evaluation

dialoflow icon dialoflow

Code for ACL 2021 main conference paper "Conversations Are Not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances".

dialog-eval icon dialog-eval

Evaluate your dialog model with 17 metrics! (see paper)

easl icon easl

Efficient Annotation of Scalar Labels

evaluation icon evaluation

Microservice to handle automatic evaluation of neural chatbot models. Multiple automated evaluation methods (including embedding-based metrics).

gptscore icon gptscore

Source Code of Paper "GPTScore: Evaluate as You Desire"

kani icon kani

kani (カニ) is a highly hackable microframework for chat-based language models with tool usage/function calling.

mephisto icon mephisto

A suite of tools for managing crowdsourcing tasks from the inception through to data packaging for research use

multirefeval icon multirefeval

Code and Data for the paper Investigating Evaluation of Open-Domain Dialogue Systems With Human Generated Multiple References SIGdial 2019

online_dialog_eval icon online_dialog_eval

Code for the paper "Learning an Unreferenced Metric for Online Dialogue Evaluation", ACL 2020

rankme icon rankme

The dataset and code released with the submission of NAACL 2018 paper "RankME: Reliable Human Ratings for Natural Language Generation"

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