Exploration of multilingual prompts in document-grounded dialogue (2023)

Abstract: Transferring DGD models from high-resource languages to low-resource languages is a meaningful but challenging task. Being able to provide multilingual responses to multilingual documents further complicates the task. This paper describes our method at DialDoc23 Shared Task (Document-Grounded Dialogue and Conversational Question Answering) for generate responses based on the most relevant passage retrieved. We divide it into three steps of retrieval, re-ranking and generation. Our methods include negative sample augmentation, prompt learning, pseudo-labeling and ensemble. On the submission page, we rank 2nd based on the sum of token-level F1, SacreBleu and Rouge-L scores used for the final evaluation, and get the total score of 210.25.

## introduction our team fanjuanju participates in the third dial-doc workshop shared task co-located with acl 2023. the goal of this task is to query document knowledge through a multilingual dialogue system. the dataset contains 797 dialogues in vietnamese (3,446 turns), 816 dialogues in french (3,510 turns), and a corpus of 17272 paragraphs, that each dialogue turn is grounded in a paragraph from the corpus. we need to use the dialogue history and the current query to retrieval the paragraph that supports the answer to the current question, and generate corresponding responses based on the knowledge in the paragraph. the score is calculated based on the sum of token-level f1 @xcite , sacrebleu @xcite and rouge-l metrics, hereinafter referred to as f1, bleu, rouge respectively. ## method according to baseline @xcite , we divide the tasks into three steps: retrieval, re-ranking and generation. firstly, we use the method of contrast learning to train the retrieval model, and expand the negative example in the training process to improve the performance of the retrieval model. in the re-ranking step, we fine-tune the xlm-roberta @xcite and infoxlm @xcite models, then ensemble the two models to predict the scores of the retrieved paragraphs. in the generation step, we use the prompt learning method to fine-tune mt5 @xcite to generate the corresponding language response, and finally add the pseudo-tag retraining to get the final response. the framework we used in this competition is illustrated in figure 1 . ## conclusion we have introduced our submission for the third dialdoc workshop shared task. our team ranks 2nd on the final submission page. we have made improvements on the baseline and tried programs such as negative sample augmentation, ensemble, prompt learning, adversarial training, and pseudotagging. there are other methods that could further improve the performance of our model. try to translate the official chinese and english data into vietnamese and french, and then use all available vietnamese and french data for pre-training. you can try to combine the retrieved top k with the reordered relevancy for weighted ranking. try combining a reorder task with a build task for training. you can try training a dichotomous model to score the generated statements to pick out the responses with the highest scores. because of time and equipment constraints, we didn't try everything during the competition. we hope the above methods can be helpful to future contestants.