Donghoon Ham


2022

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Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models
Sanghwan Bae | Donghyun Kwak | Sungdong Kim | Donghoon Ham | Soyoung Kang | Sang-Woo Lee | Woomyoung Park
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Recent open-domain dialogue models have brought numerous breakthroughs. However, building a chat system is not scalable since it often requires a considerable volume of human-human dialogue data, especially when enforcing features such as persona, style, or safety. In this work, we study the challenge of imposing roles on open-domain dialogue systems, with the goal of making the systems maintain consistent roles while conversing naturally with humans. To accomplish this, the system must satisfy a role specification that includes certain conditions on the stated features as well as a system policy on whether or not certain types of utterances are allowed. For this, we propose an efficient data collection framework leveraging in-context few-shot learning of large-scale language models for building role-satisfying dialogue dataset from scratch. We then compare various architectures for open-domain dialogue systems in terms of meeting role specifications while maintaining conversational abilities. Automatic and human evaluations show that our models return few out-of-bounds utterances, keeping competitive performance on general metrics. We release a Korean dialogue dataset we built for further research.

2021

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What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers
Boseop Kim | HyoungSeok Kim | Sang-Woo Lee | Gichang Lee | Donghyun Kwak | Jeon Dong Hyeon | Sunghyun Park | Sungju Kim | Seonhoon Kim | Dongpil Seo | Heungsub Lee | Minyoung Jeong | Sungjae Lee | Minsub Kim | Suk Hyun Ko | Seokhun Kim | Taeyong Park | Jinuk Kim | Soyoung Kang | Na-Hyeon Ryu | Kang Min Yoo | Minsuk Chang | Soobin Suh | Sookyo In | Jinseong Park | Kyungduk Kim | Hiun Kim | Jisu Jeong | Yong Goo Yeo | Donghoon Ham | Dongju Park | Min Young Lee | Jaewook Kang | Inho Kang | Jung-Woo Ha | Woomyoung Park | Nako Sung
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

GPT-3 shows remarkable in-context learning ability of large-scale language models (LMs) trained on hundreds of billion scale data. Here we address some remaining issues less reported by the GPT-3 paper, such as a non-English LM, the performances of different sized models, and the effect of recently introduced prompt optimization on in-context learning. To achieve this, we introduce HyperCLOVA, a Korean variant of 82B GPT-3 trained on a Korean-centric corpus of 560B tokens. Enhanced by our Korean-specific tokenization, HyperCLOVA with our training configuration shows state-of-the-art in-context zero-shot and few-shot learning performances on various downstream tasks in Korean. Also, we show the performance benefits of prompt-based learning and demonstrate how it can be integrated into the prompt engineering pipeline. Then we discuss the possibility of materializing the No Code AI paradigm by providing AI prototyping capabilities to non-experts of ML by introducing HyperCLOVA studio, an interactive prompt engineering interface. Lastly, we demonstrate the potential of our methods with three successful in-house applications.

2020

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End-to-End Neural Pipeline for Goal-Oriented Dialogue Systems using GPT-2
Donghoon Ham | Jeong-Gwan Lee | Youngsoo Jang | Kee-Eung Kim
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

The goal-oriented dialogue system needs to be optimized for tracking the dialogue flow and carrying out an effective conversation under various situations to meet the user goal. The traditional approach to build such a dialogue system is to take a pipelined modular architecture, where its modules are optimized individually. However, such an optimization scheme does not necessarily yield the overall performance improvement of the whole system. On the other hand, end-to-end dialogue systems with monolithic neural architecture are often trained only with input-output utterances, without taking into account the entire annotations available in the corpus. This scheme makes it difficult for goal-oriented dialogues where the system needs to integrate with external systems or to provide interpretable information about why the system generated a particular response. In this paper, we present an end-to-end neural architecture for dialogue systems that addresses both challenges above. In the human evaluation, our dialogue system achieved the success rate of 68.32%, the language understanding score of 4.149, and the response appropriateness score of 4.287, which ranked the system at the top position in the end-to-end multi-domain dialogue system task in the 8th dialogue systems technology challenge (DSTC8).