Eric Davis


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What, When, and How to Ground: Designing User Persona-Aware Conversational Agents for Engaging Dialogue
Deuksin Kwon | Sunwoo Lee | Ki Hyun Kim | Seojin Lee | Taeyoon Kim | Eric Davis
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)

This paper presents a method for building a personalized open-domain dialogue system to address the WWH (WHAT, WHEN, and HOW) problem for natural response generation in a commercial setting, where personalized dialogue responses are heavily interleaved with casual response turns. The proposed approach involves weighted dataset blending, negative persona information augmentation methods, and the design of personalized conversation datasets to address the challenges of WWH in personalized, open-domain dialogue systems. Our work effectively balances dialogue fluency and tendency to ground, while also introducing a response-type label to improve the controllability and explainability of the grounded responses. The combination of these methods leads to more fluent conversations, as evidenced by subjective human evaluations as well as objective evaluations.


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KoBEST: Korean Balanced Evaluation of Significant Tasks
Myeongjun Jang | Dohyung Kim | Deuk Sin Kwon | Eric Davis
Proceedings of the 29th International Conference on Computational Linguistics

A well-formulated benchmark plays a critical role in spurring advancements in the natural language processing (NLP) field, as it allows objective and precise evaluation of diverse models. As modern language models (LMs) have become more elaborate and sophisticated, more difficult benchmarks that require linguistic knowledge and reasoning have been proposed. However, most of these benchmarks only support English, and great effort is necessary to construct benchmarks for other low resource languages. To this end, we propose a new benchmark named Korean balanced evaluation of significant tasks (KoBEST), which consists of five Korean-language downstream tasks. Professional Korean linguists designed the tasks that require advanced Korean linguistic knowledge. Moreover, our data is purely annotated by humans and thoroughly reviewed to guarantee high data quality. We also provide baseline models and human performance results. Our dataset is available on the Huggingface.


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High-accuracy Annotation and Parsing of CHILDES Transcripts
Kenji Sagae | Eric Davis | Alon Lavie | Brian MacWhinney | Shuly Wintner
Proceedings of the Workshop on Cognitive Aspects of Computational Language Acquisition