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Papers by eunseong choi

Research paper thumbnail of Multi-Granularity Guided Fusion-in-Decoder

arXiv (Cornell University), Apr 3, 2024

Research paper thumbnail of MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories

arXiv (Cornell University), Apr 28, 2021

Automated metaphor detection is a challenging task to identify the metaphorical expression of wor... more Automated metaphor detection is a challenging task to identify the metaphorical expression of words in a sentence. To tackle this problem, we adopt pre-trained contextualized models, e.g., BERT and RoBERTa. To this end, we propose a novel metaphor detection model, namely metaphor-aware late interaction over BERT (MelBERT). Our model not only leverages contextualized word representation but also benefits from linguistic metaphor identification theories to detect whether the target word is metaphorical. Our empirical results demonstrate that MelBERT outperforms several strong baselines on four benchmark datasets, i.e., VUA-18, VUA-20, MOH-X, and TroFi.

Research paper thumbnail of Forgetting-aware Linear Bias for Attentive Knowledge Tracing

Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

Research paper thumbnail of Improving the Performance of Knowledge Tracing Models using Quantized Correctness Embeddings

Research paper thumbnail of ConQueR: Contextualized Query Reduction using Search Logs

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

Query reformulation is a key mechanism to alleviate the linguistic chasm of query in ad-hoc retri... more Query reformulation is a key mechanism to alleviate the linguistic chasm of query in ad-hoc retrieval. Among various solutions, query reduction effectively removes extraneous terms and specifies concise user intent from long queries. However, it is challenging to capture hidden and diverse user intent. This paper proposes Contextualized Query Reduction (ConQueR) using a pre-trained language model (PLM). Specifically, it reduces verbose queries with two different views: core term extraction and sub-query selection. One extracts core terms from an original query at the term level, and the other determines whether a sub-query is a suitable reduction for the original query at the sequence level. Since they operate at different levels of granularity and complement each other, they are finally aggregated in an ensemble manner. We evaluate the reduction quality of ConQueR on real-world search logs collected from a commercial web search engine. It achieves up to 8.45% gains in exact match scores over the best competing model. 1 CCS CONCEPTS • Information systems → Information retrieval query processing.

Research paper thumbnail of Long-tail Mixup for Extreme Multi-label Classification

Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Research paper thumbnail of SpaDE

Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Research paper thumbnail of MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Research paper thumbnail of Multi-Granularity Guided Fusion-in-Decoder

arXiv (Cornell University), Apr 3, 2024

Research paper thumbnail of MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories

arXiv (Cornell University), Apr 28, 2021

Automated metaphor detection is a challenging task to identify the metaphorical expression of wor... more Automated metaphor detection is a challenging task to identify the metaphorical expression of words in a sentence. To tackle this problem, we adopt pre-trained contextualized models, e.g., BERT and RoBERTa. To this end, we propose a novel metaphor detection model, namely metaphor-aware late interaction over BERT (MelBERT). Our model not only leverages contextualized word representation but also benefits from linguistic metaphor identification theories to detect whether the target word is metaphorical. Our empirical results demonstrate that MelBERT outperforms several strong baselines on four benchmark datasets, i.e., VUA-18, VUA-20, MOH-X, and TroFi.

Research paper thumbnail of Forgetting-aware Linear Bias for Attentive Knowledge Tracing

Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

Research paper thumbnail of Improving the Performance of Knowledge Tracing Models using Quantized Correctness Embeddings

Research paper thumbnail of ConQueR: Contextualized Query Reduction using Search Logs

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

Query reformulation is a key mechanism to alleviate the linguistic chasm of query in ad-hoc retri... more Query reformulation is a key mechanism to alleviate the linguistic chasm of query in ad-hoc retrieval. Among various solutions, query reduction effectively removes extraneous terms and specifies concise user intent from long queries. However, it is challenging to capture hidden and diverse user intent. This paper proposes Contextualized Query Reduction (ConQueR) using a pre-trained language model (PLM). Specifically, it reduces verbose queries with two different views: core term extraction and sub-query selection. One extracts core terms from an original query at the term level, and the other determines whether a sub-query is a suitable reduction for the original query at the sequence level. Since they operate at different levels of granularity and complement each other, they are finally aggregated in an ensemble manner. We evaluate the reduction quality of ConQueR on real-world search logs collected from a commercial web search engine. It achieves up to 8.45% gains in exact match scores over the best competing model. 1 CCS CONCEPTS • Information systems → Information retrieval query processing.

Research paper thumbnail of Long-tail Mixup for Extreme Multi-label Classification

Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Research paper thumbnail of SpaDE

Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Research paper thumbnail of MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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