Cross-lingual Data Augmentation for Document-grounded Dialog Systems in Low Resource Languages (2023)

Abstract: This paper proposes a framework to address the issue of data scarcity in Document-Grounded Dialogue Systems(DGDS). Our model leverages high-resource languages to enhance the capability of dialogue generation in low-resource languages. Specifically, We present a novel pipeline CLEM (Cross-Lingual Enhanced Model) including adversarial training retrieval (Retriever and Re-ranker), and Fid (fusion-in-decoder) generator. To further leverage high-resource language, we also propose an innovative architecture to conduct alignment across different languages with translated training. Extensive experiment results demonstrate the effectiveness of our model and we achieved 4th place in the DialDoc 2023 Competition. Therefore, CLEM can serve as a solution to resource scarcity in DGDS and provide useful guidance for multi-lingual alignment tasks.

## introduction document-grounded dialogue system (dgds) is a meaningful yet challenging task, which not only allows content accessible to end users via various conversational interfaces, but also requires generating faithful responses according to knowledge resources. however, in real-world scenarios, we may not have abundant resources to construct an effective dialogue system due to the low resources of some minority languages such as vietnamese and french. previous works only consider building a dgds in high-resource languages with rich document resources such as english and chinese @xcite , which is contrary to real-world situations. extensive minority languages struggle to build well-founded chatbots due to the low resource of documents. therefore, how to generate evidential responses under a scarce resources setting deserves our attention. to address this issue, we propose a novel architecture to leverage high-resource languages to supplement low-resource languages, in turn, build a fact-based dialogue system. thus, our model can not only handle high-resource scenarios but also generate faithful responses under low-resource settings.our key contributions can be split into three parts: ## methodology we adopt the retrieve-rerank-generation architecture @xcite and incorporate adversarial training into both the retriever and re-ranker components. to address the low-resource dgds scenario, we propose a novel three-stage training approach. ## experiments and results in this section, we will introduce our datasets and baseline system. additionally, we will demonstrate the effectiveness of each component in our methodology, such as adversarial training and the novel training process. ## conclusion this paper introduces clem, a novel pipeline for document-grounded dialogue systems that uses a "retrieve, re-rank, and generate" approach. to address the issue of low performance due to limited training data, we extend the adversarial training to the document retriever and re-ranker components. additionally, clem leverages highresource languages to improve low-resource languages and develops a new training process under data-scarce settings. experimental results demonstrate that clem outperforms the strong, competitive baseline and achieved 4th place on the leaderboard of the third dialdoc competition. these findings provide a promising approach for generating grounded dialogues in multilingual settings with limited training data and further demonstrate the effectiveness of leveraging high-resource languages for lowresource language enhancement. @xmath0.1 @xmath1 a.3 hyper-parameters for generator @xmath2.1 @xmath3.1 @xmath4