The goal of document-grounded dialogue (DocGD) is to generate a response by grounding
the evidence in a supporting document in accordance with the dialogue context. This …

Today's open-domain conversational agents increase the informativeness of generated
responses by leveraging external knowledge. Most of the existing approaches work only for …

Collecting data for training dialog systems can be extremely expensive due to the
involvement of human participants and need for extensive annotation. Especially in …

Multilingual document-grounded dialogue, where the system is required to generate
responses based on both the conversation Multilingual context and external knowledge …

Document assistant chatbots are empowered with extensive capabilities by Large Language
Models (LLMs) and have exhibited significant advancements. However, these systems may …

Dialogue segmentation is a crucial task for dialogue systems allowing a better
understanding of conversational texts. Despite recent progress in unsupervised dialogue …

This paper proposes a novel task on commonsense-enhanced task-based dialogue
grounded in documents and describes the Task2Dial dataset, a novel dataset of document …

Despite the striking advances in recent language generation performance, model-generated
responses have suffered from the chronic problem of hallucinations that are either untrue or …

In multi-hop question answering (MH-QA), the machine needs to infer the answer to a given
question from multiple documents. Existing models usually apply entities as basic units in …

We present the results of Shared Task at Workshop DialDoc 2021 that is focused on
document-grounded dialogue and conversational question answering. The primary goal of …