DSTC11: Dialogue System Technology Challenge 11
Track 4: Robust and Multilingual Automatic Evaluation Metrics for Open-Domain Dialogue Systems
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Click here to download DSTC11.T4 data. (now available)
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- Track Details
This track consists of two tasks which are explained in more detail below:
- Participants will develop effective automatic open-ended and multilingual dialogue evaluation metrics that perform similarly when evaluated over a new language.
- Participants will develop effective automatic open-ended dialogue evaluation metrics that perform robustly when evaluated over back-translated/paraphrased sentences in English.
- Correlated to human judgments - the metrics should produce evaluation scores that well correlate to human judgments (scores) across multiple languages or alternative responses (i.e., back-translated or paraphrased).
- Explainable - the metrics should provide constructive and explicit feedback to the generative models in terms of the quality of their generated responses. For instance, if a generative model is contradicting itself, the evaluation metrics should signal such behavior to the generative models.
For each evaluation task, Spearman correlation will be computed to compare the proposed evaluation metrics against human judgments. A final average score will be calculated to rank the submitted evaluation metrics.
For more information check the Track Proposal.
See the Track GitHub for more details.
- Task 1: Metrics for Multilingual Data
In this task, the goal for participants is to propose effective automatic dialogue evaluation metrics that exhibit previously mentioned properties (section 2) and perform well on a multilingual setup (English, Spanish and Chinese). In concrete, participants will propose a single multilingual model obtaining high correlations with human-annotations when evaluated on multilingual dialogues (development set in section 2.1) and perform well on the hidden multilingual test set. Participants are expected to use pre-trained multilingual models and train them to predict multidimensional quality metrics by using self-supervised techniques and optionally fine-tune their system over a subset of the development data.
Finally, participants will then evaluate their models over the development and test sets, and expect to show similar performance, in terms of correlations with human-annotations on the English, Spanish and Chinese utterances. (Note: only dev and test sets will have human-annotations, and only test sets will be manually translated or back-translated/paraphrased to guarantee the correlations with the original human-annotations on the English data).
- Task 2: Robust Metrics
In this task, the goal for participants is to propose robust metrics for automatic evaluation of just English dialogues that exhibit previously mentioned properties (section 2) while being robust when dealing with back-translated/paraphrased English sentences. The expected performance must be on par with the correlations with human-annotations obtained over the original sentences. As robustness criteria proposed, back-translated/paraphrased sentences should have the same semantic meaning as the original sentence, but different wording.
Additionally, participants will have the opportunity of testing robustness over alternative machine translations that the organizers will provide. Finally, the influence on the metric will be also evaluated when providing the back-translated/paraphrased current turn sentences instead of the original ones, always along with their respective back-translated/paraphrased context.
During the test phase, hidden and manually curated back-translated test data will be provided to participants to evaluate their proposed metrics.
- Provided Datasets
As development set, organizers will provide the following datasets (details in the GitHub section "Annex: Existing Datasets for Benchmarking") identified during the DSTC10 Track 5 (Zhang et al, 2021), that sum up more than 35k turn-level human-annotations, which have been automatically translated to Spanish and Chinese, and back-translated both to English using MS Azure services.
- CONVAI2-GRADE (CG) (Huang et al., 2020)
- DAILYDIALOG-GRADE (DH) (Huang et al., 2020)
- DAILYDIALOG-GUPTA (DG) (Gupta et al., 2019)
- DAILYDIALOG-ZHAO (DZ) (Zhao et al., 2020)
- DSTC7 (D7) (Galley et al., 2019)
- EMPATHETIC-GRADE (EG) (Huang et al., 2020)
- FED-DIAL (FD) (Mehri & Eskenazi, 2020a)
- FED-TURN (FT) (Mehri & Eskenazi, 2020a)
- HUMOD (HM) (Merdivan et al., 2020)
- PERSONA-SEE (PS) (See et al., 2019)
- PERSONA-USR (PU) (Mehri & Eskenazi, 2020b)
- PERSONA-ZHAO (PZ) (Zhao et al., 2020)
- TOPICAL-USR (TU) (Mehri & Eskenazi, 2020b)
Additionally, after the organizers' participation in the CHANEL@JSALT2020 workshop (Rudnicky et al., 2020) at John Hopkins University, they have automatically translated back-and-forth (using the same MS Azure translation service) a total of 18 well-known human-human dialogue datasets. The total amount of dialogues is 393k (approx. 3M turns).
- DBDC (Higashinaka et al., 2016)
- CMU_DoG (Zhou et al., 2018)
- Cornell Movie-Dialogs (Danescu-Niculescu-Mizil & Lee, 2011)
- DailyDialog (Li et al., 2017)
- DECODE (Nie et al., 2020)
- EmotionLines (Chen et al., 2018)
- EmpathicDialogues (Rashkin et al., 2018)
- Holl-E (Moghe et al., 2018)
- MEENA (Adiwardana et al., 2020)
- MELD (Poria et al., 2019)
- MetalWOz (Lee et al., 2019)
- Movie-DiC (Banchs, 2012)
- PersonaChat (Zhang et al., 2018)
- SentimentLIAR (Upadhayay & Behzadan, 2020)
- Switchboard Coherence (Cervone & Riccardi, 2020)
- Topical-Chat (Gopalakrishnan et al., 2019)
- Wizard of Wikipedia (Dinan et al., 2019)
- Wochat (D'Haro et al., 2016)
- ECM (Zhou et al., 2018)
- KdConv (Zhou et al., 2020)
- LCCC (Wang et al., 2020)
Since the quality of the back-translated sentences can play an important role in estimating the metric scores. QE metric scores will be given to the participants using our QE system and other existing models (e.g., COMET (Rei et al., 2020)). This information will be given to participants so they can optionally use it for discarding dialogues or turns that do not show high quality when training their metrics. Participants will be welcome to use the data and ideas from the MT field to propose QE metrics that can, optionally, be included to provide final scores. Finally, the organizers may provide new translated dialogue datasets to allow participants to create more robust and better-trained systems.
Regarding the paraphrases, all the original English sentences of each dataset will have multiple paraphrases, as well as annotations so that each participant can evaluate the quality of each paraphrase. The model used will be PARROT (Damodaran P., 2021).
Additionally, ~2k random H-H turns of CDial in Chinese were manually annotated by Tencent AI. Also, ~5k new H-C Chinese turns (~500 dialogues) were generated with three different SotA chatbots (Tencent's model, Microsoft's Xiaoice (Zhou et al., 2020) and Baidu's Plato (Bao et al., 2019)) and manually annotated by Tencent AI.
During the test phase, a new set of 2k turn-level manually curated multilingual corpus (Spanish and Chinese) together with their human-evaluation annotations will be provided to participants to test models for both tasks. This corpus will be manually checked to guarantee its quality and high correlation with the original dialogues. Besides, in order to check the generalization capabilities of the proposed metrics from the participant, the test data will include a new dataset of human-chatbot interactions and their annotations.
- Datasets Information
|Language||English, Spanish/Chinese, and English back-translation||English, Spanish/Chinese, and English back-translation||Chinese, English, and Chinese back-translation|
|Dialogues Type||Human-Human Open-Domain||Human-Chatbot Open-Domain||Human-Human Open-Domain|
|# Dialogues/ Utterances||+ 390.000 / + 3.000.000||+ 18.000 / + 55.000||+ 3.470 / +130.000|
|Annotations||Sentiment analysis and Toxicity||Turn/dialogue level human scores||Turn level human scores|
|Task 1 Set||Public: Train||Public: Dev, Test Hidden: Automatic Translations||Public: Train|
|Task 2 Set||Public: Train||Public: Dev, Test Hidden: Manually back-translation/paraphrased||—|
- Datasets Statistics
CHANEL dataset is Task 1 and Task 2 oriented. The source language is English.
|Wizard of Wikipedia||✔||✔||✔||✔||✔||✔|
DSTC10 dataset is Task 1 and Task 2 oriented. The source language is English.
CDIAL dataset is Task 1 oriented. The source language is Chinese.
- Data Format
All data given follows the Data Formats which provides guidelines on how to store, maintain and handle dialogue corpora.
- Baseline Model
The purpose of this project is to identify a baseline classifier for DSTC-11. The default choice is Deep AM-FM (Zhang et al, 2020) (used for DSTC-10 and previously).
This project will investigate more recent approaches, based on fine-tuned large language models. Zhang et al note that their approach may be limited due to domain specificity. On the other hand LLMs are trained from large corpora that in priciple are less domain-dependent. This is an empirical question.
All information related to the baseline model, such as code and data, can be found in this GitHub repository.
- Automatic Evaluation Leaderboard
The leaderboard shows names of submissions and their corresponding Spearman Correlation Coefficients for each development dataset. The name of each column corresponds to an abbreviation of the development datasets respectively.
Task 1: Metrics for Multilingual Data (development)
Task 2: Robust Metrics (development)
- Training/Validation data release: From November to December in 2022
- Test data release: Middle of March in 2023
- Entry submission deadline: Middle of March in 2023
- Submission of final results: End of March in 2023
- Final result announcement: Early of April in 2023
- Paper submission: From March to May in 2023
- Workshop: July, August or September in 2023
To become an official DSTC11 Track 4 participant, you must be registered at this Microsoft Form. Once registered, you will be able to download the datasets and readme documents as well as submit your results at https://chateval.org/dstc11.
There must be only one team per laboratory or research group. The members of the same team must be under a single registration, that is, the team leader must register his entire team by giving their e-mail addresses in addition to his own.
Before submitting your model, do not forget to Sign Up on the ChatEval website. Only the team leader must register on ChatEval, with the same name and email address entered in the Microsoft Form. Once you have signed up, you can Log In and Submit your models.
You can make as many submissions as you want, but only the last 5 submitted will be valid to participate in the competition. That is, only the last 5 models submitted in ChatEval will be tested and will count in the competition ranking. Only the models submitted by the team leader registered in the Microsoft Form will be considered and tested during the competition.
- Mario Rodríguez-Cantelar (Universidad Politécnica de Madrid, Spain)
- Chen Zhang (National University of Singapore, Singapore)
- Chengguang Tang (Tencent AI Lab, China)
- Ke Shi (Tencent AI Lab, China)
- João Sedoc (New York University, USA)
- Luis F. D'Haro (Universidad Politécnica de Madrid, Spain)
- Alexander Rudnicky (Carnegie Mellon University, USA)
If you have further questions regarding the data, please let us know by the following email address at email@example.com.
This research project is supported by the Comunidad de Madrid through the call Research Grants for Young Investigators from Universidad Politécnica de Madrid (GENIUS:APOYO-JOVENES-21-TAXTYC-32-K61X37).
This work is supported by project BEWORD (PID2021-126061OB-C43) funded by MCIN/AEI/10.13039/501100011033 and, as appropriate, by “ERDF A way of making Europe”, by the “European Union”, and by Programa Propio - Proyectos Semilla: Universidad Politécnica de Madrid (VSEMILLA22LFHE).
We gratefully acknowledge valuable efforts from Tencent AI Lab who supports Chinese translation and annotation of datasets by funding and infrastructure.
Thanks to THU-CoAI (Conversational AI groups from Tsinghua University) for providing their Chinese datasets as part of the challenge data.
Thanks to Unbabel for providing the COMET MTQE scores annotations as part of the challenge data. This contribution was supported by national funds through *Fundação para a Ciência e a Tecnologia* (FCT) with references PRT/BD/152198/2021 and UIDB/50021/2020, and by the P2020 program MAIA led by Unbabel (LISBOA-01-0247-FEDER-045909).
We also want to give thanks to MS Azure services (especially to Irving Kwong) for their sponsorship to continue processing new datasets that could be interesting for the dialogue community.
This research project is supported by the NYU ChatEval Team led by João Sedoc.
This research project is supported in part by a grant from Amazon to Alexander Rudnicky, Carnegie Mellon University.
Thanks to Karthik Ganesan, Sarik Ghazarian, James Hagerty, Zhang Chen and Alex Rudnicky for developing the baseline model as part of the challenge tasks.
This work is supported by the European Commission through Project ASTOUND (101071191 — HORIZON-EIC-2021-PATHFINDERCHALLENGES-01).
How much does participate in this Track cost?
This Track is currently free for everyone.
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