--- title: FrDoc2BotRetrieval canonical_url: "https://www.modelscope.cn/datasets/DAMO_ConvAI/FrDoc2BotRetrieval" md_url: "https://www.modelscope.cn/datasets/DAMO_ConvAI/FrDoc2BotRetrieval.md" repository: DAMO_ConvAI/FrDoc2BotRetrieval chinese_name: "French Document-grounded Dialogue Retrieval Dataset (法语对话检索数据集)" last_updated: 2024-09-02 license: "Apache License 2.0" storage_size: "3.8 MB" domain: - configs - text tasks: - "{config_name=FrDoc2BotRetrieval, data_files=[{path=retrieval_train_fr.csv, split=train}]}" - conversational size_scale: - 100-10k type: - kg-qa downloads: 26833 stars: 0 --- # FrDoc2BotRetrieval > FrDoc2BotRetrieval - DAMO_ConvAI 在 ModelScope 开源的数据集。French Document-grounded Dialogue Retrieval Dataset (法语对话检索数据集) DAMO_ConvAI/FrDoc2BotRetrieval 是 ModelScope 魔搭社区上的{config_name=FrDoc2BotRetrieval, data_files=[{path=retrieval_train_fr.csv, split=train}]}、conversational数据集，涉及 configs、text 领域，存储大小 3.8 MB，采用 Apache License 2.0 许可。 - **Repository**: DAMO_ConvAI/FrDoc2BotRetrieval - **License**: Apache License 2.0 - **Tasks**: {config_name=FrDoc2BotRetrieval, data_files=[{path=retrieval_train_fr.csv, split=train}]}, conversational - **Domain**: configs, text - **Storage size**: 3.8 MB - **Downloads**: 26833 - **Stars**: 0 - **Last updated**: 2024-09-02 Source: https://www.modelscope.cn/datasets/DAMO_ConvAI/FrDoc2BotRetrieval --- ### Clone with HTTP ```bash git clone https://www.modelscope.cn/datasets/AronXiang/FrDoc2BotRetrieval.git ``` # Documen-grounded dialogue Goal-oriented document-grounded dialogue systems enable end users to interactively query about domain-specific information based on the given documents. The tasks of querying document knowledge via conversational systems continue to attract a lot of attention from both research and industrial communities for various applications. The previous works addressed the task of English and Chinese document-grounded dialogue systems, leaving other languages less well explored. Thus, large communities of users are prevented access to automated services and information. We aim to extend the effort by introducing the Third ACL DialDoc Workshop shared task involving documents and dialogues in diverse languages. We present this multilingual DGD challenge to encourage researchers to explore effective solutions for (1) transferring a DGD model from a high-resource language to a low-resource language; (2) developing a DGD model that is capable of providing multilingual responses given multilingual documents. ### Description Specifically，we provide 797 dialogues in Vietnamese (3,446 turns), 816 dialogues in French (3,510 turns), and a corpus of 17272 paragraphs, where each dialogue turn is grounded in a paragraph from the corpus. We also organize the currently available Chinese and English document-grounded dialogue data. We hope that participants can leverage the linguistic similarities, for example, a large number of Vietnamese words are derived from Chinese, and English and French both belong to the Indo-European language family, to improve their models' performance in Vietnamese and French. So the task objective is to retrieve relevant paragraphs from a corpus based on the dialogue history and generate a response. To address this issue, we provide a baseline model consisting of three modules: retrieving the top-K relevant paragraphs from the corpus based on the dialogue history, ranking the top-N most relevant paragraphs, and concatenating them with the dialogue history to generate a response using a generation module. **This Project contains the French data for fine-tuning the retrieval module.** ### Dataset Format Each piece of data contains three attributes: query, positive, and negative. The query is a concatenation of the conversation history in reverse order, with the last turn marked as "", and the rest marked with "" for user input and "" for system output. For example： ' Quelle est la position des défenseurs du monopole de la Sécurité sociale concernant l'obligation de cotiser en France à la Sécurité sociale ? La solution proposée est de quitter la Sécurité sociale et de souscrire à une assurance privée Qu'est-ce qu'un mouvement libéral qui critique le monopole de la Sécurité sociale en France propose comme solution ?' "Positive" refers to the positive samples, which are the samples used to label the targets of the dialogue. The titles in reverse chronological order are concatenated and separated by "//". For example: 'Certains mouvements libéraux, comme le Mouvement pour la liberté de la protection sociale, critiquent le monopole de la Sécurité sociale en France et disent qu'il est possible de quitter la Sécurité sociale et de souscrire à la place à une assurance privée,. // Critique de l'obligation d'affiliation[modifier | modifier le code] // Affiliation obligatoire[modifier | modifier le code] // Assujettissement et affiliation à un régime[modifier | modifier le code] // Sécurité sociale en France — Wikipédia // fr-SocialSecurity' "Negative" refers to negative samples, which are obtained by retrieving the passage with the highest BM25 value from the dialogue history, excluding the positive samples. The format is the same as for positive samples. ### Loading Method ```python from modelscope.msdatasets import MsDataset from modelscope.utils.constant import DownloadMode dataset = MsDataset.load( 'DAMO_ConvAI/FrDoc2BotRetrieval', download_mode=DownloadMode.FORCE_REDOWNLOAD) print(dataset[0]) ```