Jinhyeon Kim


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Vacillating Human Correlation of SacreBLEU in Unprotected Languages
Ahrii Kim | Jinhyeon Kim
Proceedings of the 2nd Workshop on Human Evaluation of NLP Systems (HumEval)

SacreBLEU, by incorporating a text normalizing step in the pipeline, has become a rising automatic evaluation metric in recent MT studies. With agglutinative languages such as Korean, however, the lexical-level metric cannot provide a conceivable result without a customized pre-tokenization. This paper endeavors to ex- amine the influence of diversified tokenization schemes –word, morpheme, subword, character, and consonants & vowels (CV)– on the metric after its protective layer is peeled off. By performing meta-evaluation with manually- constructed into-Korean resources, our empirical study demonstrates that the human correlation of the surface-based metric and other homogeneous ones (as an extension) vacillates greatly by the token type. Moreover, the human correlation of the metric often deteriorates due to some tokenization, with CV one of its culprits. Guiding through the proper usage of tokenizers for the given metric, we discover i) the feasibility of the character tokens and ii) the deficit of CV in the Korean MT evaluation.

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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.