MP2D: An Automated Topic Shift Dialogue Generation Framework Leveraging Knowledge Graphs

Despite advancements in on-topic dialogue systems, effectively managing topic shifts within dialogues remains a persistent challenge, largely attributed to the limited availability of training datasets. To address this issue, we propose Multi-Passage to Dialogue (MP2D), a data generation framework that automatically creates conversational question-answering datasets with natural topic transitions. By leveraging the relationships between entities in a knowledge graph, MP2D maps the flow of topics within a dialogue, effectively mirroring the dynamics of human conversation. It retrieves relevant passages corresponding to the topics and transforms them into dialogues through the passage-to-dialogue method. Through quantitative and qualitative experiments, we demonstrate MP2D's efficacy in generating dialogue with natural topic shifts. Furthermore, this study introduces a novel benchmark for topic shift dialogues, TS-WikiDialog. Utilizing the dataset, we demonstrate that even Large Language Models (LLMs) struggle to handle topic shifts in dialogue effectively, and we showcase the performance improvements of models trained on datasets generated by MP2D across diverse topic shift dialogue tasks.Comment: 20 page

Dialogizer: Context-aware Conversational-QA Dataset Generation from Textual Sources

To address the data scarcity issue in Conversational question answering (ConvQA), a dialog inpainting method, which utilizes documents to generate ConvQA datasets, has been proposed. However, the original dialog inpainting model is trained solely on the dialog reconstruction task, resulting in the generation of questions with low contextual relevance due to insufficient learning of question-answer alignment. To overcome this limitation, we propose a novel framework called Dialogizer, which has the capability to automatically generate ConvQA datasets with high contextual relevance from textual sources. The framework incorporates two training tasks: question-answer matching (QAM) and topic-aware dialog generation (TDG). Moreover, re-ranking is conducted during the inference phase based on the contextual relevance of the generated questions. Using our framework, we produce four ConvQA datasets by utilizing documents from multiple domains as the primary source. Through automatic evaluation using diverse metrics, as well as human evaluation, we validate that our proposed framework exhibits the ability to generate datasets of higher quality compared to the baseline dialog inpainting model.Comment: Accepted to EMNLP 2023 main conferenc

Differential Epigenetic Effects of Atmospheric Cold Plasma on MCF-7 and MDA-MB-231 Breast Cancer Cells

<div><p>Cold atmospheric plasma (plasma) has emerged as a novel tool for a cancer treatment option, having been successfully applied to a few types of cancer cells, as well as tissues. However, to date, no studies have been performed to examine the effect of plasma on epigenetic alterations, including CpG methylation. In this study, the effects of plasma on DNA methylation changes in breast cancer cells were examined by treating cultured MCF-7 and MDA-MB-231 cells, representing estrogen-positive and estrogen-negative cancer cells, respectively, with plasma. A pyrosequencing analysis of <i>Alu</i> indicated that a specific CpG site was induced to be hypomethylated from 23.4 to 20.3% (p < 0.05) by plasma treatment in the estrogen-negative MDA-MB-231 cells only. A genome-wide methylation analysis identified “cellular movement, connective tissue development and function, tissue development” and “cell-to-cell signaling and interaction, cell death and survival, cellular development” as the top networks. Of the two cell types, the MDA-MB-231 cells underwent a higher rate of apoptosis and a decreased proliferation rate upon plasma treatment. Taken together, these results indicate that plasma induces epigenetic and cellular changes in a cell type-specific manner, suggesting that a careful screening of target cells and tissues is necessary for the potential application of plasma as a cancer treatment option.</p></div

RT-PCR analysis of selected genes showing altered methylation after plasma treatment in breast cancer cells.

<p>Real-time RT-PCR analysis of selected genes displaying altered methylation levels in response to plasma in the MCF-7 (A) and MDA-MB-231 cells (B). Cells were treated 10 times, 30 sec each time with an hour interval between exposures, and then harvested after 24 hr. Each sample was analyzed in triplicate, with the average relative expression levels indicated with standard errors.</p

Effect of plasma on the global methylation levels in breast cell lines.

<p>The methylation levels of the four CpGs on the <i>Alu</i> from the MCF-12A, MCF-10A, MCF-7, and MDA-MB-231 cells were determined by pyrosequencing after treatment with plasma. (A) The sequence of the <i>Alu</i> adopted in this study [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129931#pone.0129931.ref042" target="_blank">42</a>]. The three CpG sites analyzed are indicated in red and numbered. (B) Bar graphs showing the methylation levels of CpG #2 of <i>Alu</i> in the four cell lines. Five independent experiments were performed for each cell line and average values are given with the standard errors. (C) A representative pyrosequencing diagram for MDA-MB-231 cells.</p

Apoptosis and cell proliferation assays on the breast cancer cells exposed to plasma.

<p>(A) MCF-7, MDA-MB-231, and MCF-10A cells were treated with plasma for 10 times (30 sec each time with an hour interval between exposures), and apoptosis was analyzed by FACS. The assay was performed in triplicate and the result is given by a representative FACS diagram. The ratio of cells undergoing apoptosis is denoted by a bar graph with average and standard errors. (B) Results of the cell proliferation assay by using Cell Counting Kit-8. Each sample was analyzed in triplicate, with the average growth rate and standard errors.</p

Genes in the top network displaying differential methylation in MDA-MB-231 cells exposed to cold plasma.

<p>Genes in the top network displaying differential methylation in MDA-MB-231 cells exposed to cold plasma.</p

Genes in the top network displaying differential methylation in MCF-7 cells exposed to cold plasma.

<p>Genes in the top network displaying differential methylation in MCF-7 cells exposed to cold plasma.</p

Cold plasma enhances ROS accumulation in breast cancer cells.

<p>Cells were treated with cold plasma for 600 sec. ROS level was quantified by the fluorescent dye DCFH-DA and is shown as a fold change relative to non-treated cells. Each sample was analyzed in triplicate, with the average relative levels indicated with standard errors.</p

The highest confidence network of genes displaying altered methylation levels induced by plasma in the breast cancer cells.

<p>According to IPA, the highest confidence networks in the MCF-7 cells (A) and MDA-MB-231 cells (B) were “cellular movement, connective tissue development and function, tissue development” and “cell-to-cell signaling and interaction, cell death and survival, cellular development”, respectively. Genes that were hypermethylated in the plasma-treated cells are shaded in red, while those that were hypomethylated are shaded in green, with intensity signifying the magnitude of methylation change. Each interaction is supported by at least one literature reference, with solid lines representing direct interactions and dashed lines representing indirect interactions.</p