N-best Response-based Analysis of Contradiction-awareness in Neural Response Generation Models

Avoiding the generation of responses that contradict the preceding context is a significant challenge in dialogue response generation. One feasible method is post-processing, such as filtering out contradicting responses from a resulting n-best response list. In this scenario, the quality of the n-best list considerably affects the occurrence of contradictions because the final response is chosen from this n-best list. This study quantitatively analyzes the contextual contradiction-awareness of neural response generation models using the consistency of the n-best lists. Particularly, we used polar questions as stimulus inputs for concise and quantitative analyses. Our tests illustrate the contradiction-awareness of recent neural response generation models and methodologies, followed by a discussion of their properties and limitations.Comment: 8 pages, Accepted to The 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL 2022

A Case of Bilateral Adrenal and Pleural Metastases from Prostate Cancer

Our case was 65 years old. At check-up, a high PSA level of 515 ng/ml was observed, the patient was diagnosed with having clinical stage D prostate cancer and a Maximum Androgen Blockade (MAB therapy) was started. In response to the exacerbated prostate cancer, we started a therapy involving the administration of 8 mg/kg body weight of dexamethasone and 55 mg/m2 of docetaxel every 3 weeks. After completing 8 courses, an enlargement of the bilateral adrenal tumor was observed, and after completing 12 courses, a pleural tumor was discovered and the PSA level was also increased. The patient was therefore diagnosed with having bilateral adrenal metastasis and pleural metastasis of prostate cancer through diagnostic imaging. So far, there have been no reports of multiple occurrences of prostate cancer in the adrenal glands and the pleura, thus making this case the first such case

Arukikata Travelogue Dataset with Geographic Entity Mention, Coreference, and Link Annotation

Geoparsing is a fundamental technique for analyzing geo-entity information in text. We focus on document-level geoparsing, which considers geographic relatedness among geo-entity mentions, and presents a Japanese travelogue dataset designed for evaluating document-level geoparsing systems. Our dataset comprises 200 travelogue documents with rich geo-entity information: 12,171 mentions, 6,339 coreference clusters, and 2,551 geo-entities linked to geo-database entries