Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams

In this work, we introduce a new algorithm for analyzing a diagram, which contains visual and textual information in an abstract and integrated way. Whereas diagrams contain richer information compared with individual image-based or language-based data, proper solutions for automatically understanding them have not been proposed due to their innate characteristics of multi-modality and arbitrariness of layouts. To tackle this problem, we propose a unified diagram-parsing network for generating knowledge from diagrams based on an object detector and a recurrent neural network designed for a graphical structure. Specifically, we propose a dynamic graph-generation network that is based on dynamic memory and graph theory. We explore the dynamics of information in a diagram with activation of gates in gated recurrent unit (GRU) cells. On publicly available diagram datasets, our model demonstrates a state-of-the-art result that outperforms other baselines. Moreover, further experiments on question answering shows potentials of the proposed method for various applications

Proficiency Effects on Relative Roles of Vocabulary and Grammar Knowledge in Second Language Reading *

. Proficiency effects on relative roles of vocabulary and grammar knowledge in second language reading. English Teaching, 70(1), 75-96. The present study examines L2 reading proficiency effects on the relative contribution of vocabulary knowledge and grammar knowledge to L2 reading comprehension for Korean high school EFL learners. To this end, 200 high school students were asked to take a vocabulary knowledge test, a grammar test, and a reading comprehension test. The participants were divided into three sub-groups by L2 reading ability in order to examine L2 proficiency effects. Multiple regression analyses for the sub-groups indicated the relationships among the three variables as distinctive. The results showed that syntactic knowledge had a predictive power for reading performance in the high reading group, but vocabulary had the same quality in the intermediate reading group. For the low reading group, neither vocabulary nor grammar could significantly account for the L2 reading variance. Theoretical implications and directions for further studies are discussed

SAVE: Protagonist Diversification with Structure Agnostic Video Editing

Driven by the upsurge progress in text-to-image (T2I) generation models, text-to-video (T2V) generation has experienced a significant advance as well. Accordingly, tasks such as modifying the object or changing the style in a video have been possible. However, previous works usually work well on trivial and consistent shapes, and easily collapse on a difficult target that has a largely different body shape from the original one. In this paper, we spot the bias problem in the existing video editing method that restricts the range of choices for the new protagonist and attempt to address this issue using the conventional image-level personalization method. We adopt motion personalization that isolates the motion from a single source video and then modifies the protagonist accordingly. To deal with the natural discrepancy between image and video, we propose a motion word with an inflated textual embedding to properly represent the motion in a source video. We also regulate the motion word to attend to proper motion-related areas by introducing a novel pseudo optical flow, efficiently computed from the pre-calculated attention maps. Finally, we decouple the motion from the appearance of the source video with an additional pseudo word. Extensive experiments demonstrate the editing capability of our method, taking a step toward more diverse and extensive video editing.Comment: Project website: https://ldynx.github.io/SAVE

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

Adjuvant Chemotherapy in Microsatellite Instability-High Gastric Cancer

Purpose Microsatellite instability (MSI) status may affect the efficacy of adjuvant chemotherapy in gastric cancer. In this study, the clinical characteristics of MSI-high (MSI-H) gastric cancer and the predictive value of MSI-H for adjuvant chemotherapy in large cohorts of gastric cancer patients were evaluated. Materials and Methods This study consisted of two cohorts. Cohort 1 included gastric cancer patients who received curative resection with pathologic stage IB-IIIC. Cohort 2 included patients with MSI-H gastric cancer who received curative resection with pathologic stage II/III. MSI was examined using two mononucleotide markers and three dinucleotide markers. Results Of 359 patients (cohort 1), 41 patients (11.4%) had MSI-H. MSI-H tumors were more frequently identified in older patients (p < 0.001), other histology than poorly cohesive, signet ring cell type (p=0.005), intestinal type (p=0.028), lower third tumor location (p=0.005), and absent perineural invasion (p=0.027). MSI-H status has a tendency of better disease-free survival (DFS) and overall survival (OS) in multivariable analyses (hazard ratio [HR], 0.4; p=0.059 and HR, 0.4; p=0.063, respectively). In the analysis of 162 MSI-H patients (cohort 2), adjuvant chemotherapy showed a significant benefit with respect to longer DFS and OS (p=0.047 and p=0.043, respectively). In multivariable analysis, adjuvant chemotherapy improved DFS (HR, 0.4; p=0.040). Conclusion MSI-H gastric cancer had distinct clinicopathologic findings. Even in MSI-H gastric cancer of retrospective cohort, adjuvant chemotherapy could show a survival benefit, which was in contrast to previous prospective studies and should be investigated in a further prospective trial.