Incorporating external knowledge into dialogue generation (KIDG) is crucial
for improving the correctness of response, where evidence fragments serve as
knowledgeable snippets supporting the factual dialogue replies. However,
introducing irrelevant content often adversely impacts reply quality and easily
leads to hallucinated responses. Prior work on evidence retrieval and
integration in dialogue systems falls short of fully leveraging existing
evidence since the model fails to locate useful fragments accurately and
overlooks hidden evidence labels within the KIDG dataset. To fully Unleash the
potential of evidence, we propose a framework to effectively incorporate
Evidence in knowledge-Intensive Dialogue Generation (u-EIDG). Specifically, we
introduce an automatic evidence generation framework that harnesses the power
of Large Language Models (LLMs) to mine reliable evidence veracity labels from
unlabeled data. By utilizing these evidence labels, we train a reliable
evidence indicator to effectively identify relevant evidence from retrieved
passages. Furthermore, we propose an evidence-augmented generator with an
evidence-focused attention mechanism, which allows the model to concentrate on
evidenced segments. Experimental results on MultiDoc2Dial demonstrate the
efficacy of evidential label augmentation and refined attention mechanisms in
improving model performance. Further analysis confirms that the proposed method
outperforms other baselines (+3~+5 points) regarding coherence and factual
consistency.

整合外部知识进入对话生成中的关键性为提高回应的正确性，在这里，证据片段作为有根据的支持性知识碎片。本文提出一种有效地整合证据到知识密集型对话生成中的框架，通过引入自动证据生成框架和证据聚焦的注意机制，扩大了证据的潜力。实验结果表明，提出的方法在模型性能方面优于其他基准（+3~+5 分），在连贯性和事实一致性方面有进一步的分析证实。

释放知识密集对话生成中的证据潜力

Unleashing Potential of Evidence in Knowledge-Intensive Dialogue  Generation

Evidence plays a crucial role in any biomedical research narrative, providing
justification for some claims and refutation for others. We seek to build
models of scientific argument using information extraction methods from
full-text papers. We present the capability of automatically extracting text
fragments from primary research papers that describe the evidence presented in
that paper's figures, which arguably provides the raw material of any
scientific argument made within the paper. We apply richly contextualized deep
representation learning pre-trained on biomedical domain corpus to the analysis
of scientific discourse structures and the extraction of "evidence fragments"
(i.e., the text in the results section describing data presented in a specified
subfigure) from a set of biomedical experimental research articles. We first
demonstrate our state-of-the-art scientific discourse tagger on two scientific
discourse tagging datasets and its transferability to new datasets. We then
show the benefit of leveraging scientific discourse tags for downstream tasks
such as claim-extraction and evidence fragment detection. Our work demonstrates
the potential of using evidence fragments derived from figure spans for
improving the quality of scientific claims by cataloging, indexing and reusing
evidence fragments as independent documents.

通过从全文中提取文本片段，利用以生物医学为域的语料预训练的深度表征学习探讨科学论述结构的分析和证据片段的提取，以提高科学主张的质量。