Bipartite-play Dialogue Collection for Practical Automatic Evaluation of Dialogue Systems

Automation of dialogue system evaluation is a driving force for the efficient development of dialogue systems. This paper introduces the bipartite-play method, a dialogue collection method for automating dialogue system evaluation. It addresses the limitations of existing dialogue collection methods: (i) inability to compare with systems that are not publicly available, and (ii) vulnerability to cheating by intentionally selecting systems to be compared. Experimental results show that the automatic evaluation using the bipartite-play method mitigates these two drawbacks and correlates as strongly with human subjectivity as existing methods.Comment: 9 pages, Accepted to The AACL-IJCNLP 2022 Student Research Workshop (SRW

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

Target-Guided Open-Domain Conversation Planning

Prior studies addressing target-oriented conversational tasks lack a crucial notion that has been intensively studied in the context of goal-oriented artificial intelligence agents, namely, planning. In this study, we propose the task of Target-Guided Open-Domain Conversation Planning (TGCP) task to evaluate whether neural conversational agents have goal-oriented conversation planning abilities. Using the TGCP task, we investigate the conversation planning abilities of existing retrieval models and recent strong generative models. The experimental results reveal the challenges facing current technology.Comment: 9 pages, Accepted to The 29th International Conference on Computational Linguistics (COLING 2022

4.Rocks and minerals

Frontier Research Program for Subduction Dynamics, Japan Marine Science and Technology CenterFrontier Research Group for the Deep Sea Environment, Japan Marine Science and technology center東京大学Editor : Tazaki, Kazue, Cover:Scanning electoron microscopic photograph of Gallionella sp. in biomats of Aso caldera, Kyusyu, Japan. Various shapes of Gallonella sp. are shown (image:Moriichi, Shingo).COE, 金沢大学 水・土壌環境領域シンポジウム「地球環境における微生物の役割」, 日時：2002年12月4日（水）13：00～, 場所：金沢大学理学部3階第一実験

Activation of AMPK-Regulated CRH Neurons in the PVH is Sufficient and Necessary to Induce Dietary Preference for Carbohydrate over Fat

Food selection is essential for metabolic homeostasis and is influenced by nutritional state, food palatability, and social factors such as stress. However, the mechanism responsible for selection between a high-carbohydrate diet (HCD) and a high-fat diet (HFD) remains unknown. Here, we show that activation of a subset of corticotropin-releasing hormone (CRH)-positive neurons in the rostral region of the paraventricular hypothalamus (PVH) induces selection of an HCD over an HFD in mice during refeeding after fasting, resulting in a rapid recovery from the change in ketone metabolism. These neurons manifest activation of AMP-activated protein kinase (AMPK) during food deprivation, and this activation is necessary and sufficient for selection of an HCD over an HFD. Furthermore, this effect is mediated by carnitine palmitoyltransferase 1c (CPT1c). Thus, our results identify the specific neurons and intracellular signaling pathway responsible for regulation of the complex behavior of selection between an HCD and an HFD

A Large Collection of Model-generated Contradictory Responses for Consistency-aware Dialogue Systems

Mitigating the generation of contradictory responses poses a substantial challenge in dialogue response generation. The quality and quantity of available contradictory response data play a vital role in suppressing these contradictions, offering two significant benefits. First, having access to large contradiction data enables a comprehensive examination of their characteristics. Second, data-driven methods to mitigate contradictions may be enhanced with large-scale contradiction data for training. Nevertheless, no attempt has been made to build an extensive collection of model-generated contradictory responses. In this paper, we build a large dataset of response generation models' contradictions for the first time. Then, we acquire valuable insights into the characteristics of model-generated contradictions through an extensive analysis of the collected responses. Lastly, we also demonstrate how this dataset substantially enhances the performance of data-driven contradiction suppression methods.Comment: 16 page