DSTC11: Dialogue System Technology Challenge 11
Track 4: Robust and Multilingual Automatic Evaluation Metrics for Open-Domain Dialogue Systems
- 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.
- Provided Datasets
As development set, organizers will provide the following datasets (details in the Data Description section) 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:
- DSTC6 human evaluation data (Hori et al., 2017)
- DSTC7 human evaluation data (Galley et al., 2019)
- Persona-Chatlog dataset (See et al., 2019)
- ChatEval dataset (Sedoc et al., 2019)
- USR dataset (Mehri & Eskenazi, 2020)
- FED dataset (Mehri & Eskenazi, 2020)
- DSTC10 dataset (Zhang et al., 2021)
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 19 well-known human-human dialogue datasets:
- 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)
- KvPI (Song et al., 2020)
- 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)
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., Openkiwi (Kepler et al., 2019) or 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.
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 and Statistics
|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||—|
Data FormatAll data given follows the Unified Dialogue Data Formats which provides guidelines on how to store, maintain and handle dialogue corpora.
- 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 language 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.
- 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
Baselines and Data Description
For more information check the Track Proposal.
See the Track GitHub for more details.
You can register at https://my.chateval.org/accounts/login/, once registered, you will be able to download the datasets and readme documents as well as submit your results at https://chateval.org/dstc11.
- 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.
How much does participate in this Track cost?
This Track is currently free for everyone.
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