Update README.md

README.md

@@ -6,23 +6,9 @@ configs:
       path: "train.csv"
 language:
 - fr
-- vi
 ---
 
 
-# Document-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 generation module.**
-
-
 ### Dataset Format
 
 Each piece of data contains three attributes: query, rerank, and response.