Ji-Hoon Kim


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Two-Step Question Retrieval for Open-Domain QA
Yeon Seonwoo | Juhee Son | Jiho Jin | Sang-Woo Lee | Ji-Hoon Kim | Jung-Woo Ha | Alice Oh
Findings of the Association for Computational Linguistics: ACL 2022

The retriever-reader pipeline has shown promising performance in open-domain QA but suffers from a very slow inference speed. Recently proposed question retrieval models tackle this problem by indexing question-answer pairs and searching for similar questions. These models have shown a significant increase in inference speed, but at the cost of lower QA performance compared to the retriever-reader models. This paper proposes a two-step question retrieval model, SQuID (Sequential Question-Indexed Dense retrieval) and distant supervision for training. SQuID uses two bi-encoders for question retrieval. The first-step retriever selects top-k similar questions, and the second-step retriever finds the most similar question from the top-k questions. We evaluate the performance and the computational efficiency of SQuID. The results show that SQuID significantly increases the performance of existing question retrieval models with a negligible loss on inference speed.


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Weakly Supervised Pre-Training for Multi-Hop Retriever
Yeon Seonwoo | Sang-Woo Lee | Ji-Hoon Kim | Jung-Woo Ha | Alice Oh
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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Context-Aware Answer Extraction in Question Answering
Yeon Seonwoo | Ji-Hoon Kim | Jung-Woo Ha | Alice Oh
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Extractive QA models have shown very promising performance in predicting the correct answer to a question for a given passage. However, they sometimes result in predicting the correct answer text but in a context irrelevant to the given question. This discrepancy becomes especially important as the number of occurrences of the answer text in a passage increases. To resolve this issue, we propose BLANC (BLock AttentioN for Context prediction) based on two main ideas: context prediction as an auxiliary task in multi-task learning manner, and a block attention method that learns the context prediction task. With experiments on reading comprehension, we show that BLANC outperforms the state-of-the-art QA models, and the performance gap increases as the number of answer text occurrences increases. We also conduct an experiment of training the models using SQuAD and predicting the supporting facts on HotpotQA and show that BLANC outperforms all baseline models in this zero-shot setting.

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Scale down Transformer by Grouping Features for a Lightweight Character-level Language Model
Sungrae Park | Geewook Kim | Junyeop Lee | Junbum Cha | Ji-Hoon Kim | Hwalsuk Lee
Proceedings of the 28th International Conference on Computational Linguistics

This paper introduces a method that efficiently reduces the computational cost and parameter size of Transformer. The proposed model, refer to as Group-Transformer, splits feature space into multiple groups, factorizes the calculation paths, and reduces computations for the group interaction. Extensive experiments on two benchmark tasks, enwik8 and text8, prove our model’s effectiveness and efficiency in small-scale Transformers. To the best of our knowledge, Group-Transformer is the first attempt to design Transformer with the group strategy, widely used for efficient CNN architectures.