Kee-Eung Kim


2022

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Learning to Embed Multi-Modal Contexts for Situated Conversational Agents
Haeju Lee | Oh Joon Kwon | Yunseon Choi | Minho Park | Ran Han | Yoonhyung Kim | Jinhyeon Kim | Youngjune Lee | Haebin Shin | Kangwook Lee | Kee-Eung Kim
Findings of the Association for Computational Linguistics: NAACL 2022

The Situated Interactive Multi-Modal Conversations (SIMMC) 2.0 aims to create virtual shopping assistants that can accept complex multi-modal inputs, i.e. visual appearances of objects and user utterances. It consists of four subtasks, multi-modal disambiguation (MM-Disamb), multi-modal coreference resolution (MM-Coref), multi-modal dialog state tracking (MM-DST), and response retrieval and generation. While many task-oriented dialog systems usually tackle each subtask separately, we propose a jointly learned multi-modal encoder-decoder that incorporates visual inputs and performs all four subtasks at once for efficiency. This approach won the MM-Coref and response retrieval subtasks and nominated runner-up for the remaining subtasks using a single unified model at the 10th Dialog Systems Technology Challenge (DSTC10), setting a high bar for the novel task of multi-modal task-oriented dialog systems.

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).

2019

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PyOpenDial: A Python-based Domain-Independent Toolkit for Developing Spoken Dialogue Systems with Probabilistic Rules
Youngsoo Jang | Jongmin Lee | Jaeyoung Park | Kyeng-Hun Lee | Pierre Lison | Kee-Eung Kim
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

We present PyOpenDial, a Python-based domain-independent, open-source toolkit for spoken dialogue systems. Recent advances in core components of dialogue systems, such as speech recognition, language understanding, dialogue management, and language generation, harness deep learning to achieve state-of-the-art performance. The original OpenDial, implemented in Java, provides a plugin architecture to integrate external modules, but lacks Python bindings, making it difficult to interface with popular deep learning frameworks such as Tensorflow or PyTorch. To this end, we re-implemented OpenDial in Python and extended the toolkit with a number of novel functionalities for neural dialogue state tracking and action planning. We describe the overall architecture and its extensions, and illustrate their use on an example where the system response model is implemented with a recurrent neural network.

2014

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Optimizing Generative Dialog State Tracker via Cascading Gradient Descent
Byung-Jun Lee | Woosang Lim | Daejoong Kim | Kee-Eung Kim
Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

2013

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Engineering Statistical Dialog State Trackers: A Case Study on DSTC
Daejoong Kim | Jaedeug Choi Choi | Kee-Eung Kim | Jungsu Lee | Jinho Sohn
Proceedings of the SIGDIAL 2013 Conference