Papers by Seungtaek Choi
Evaluation of Question Generation Needs More References (2023.findings-acl)
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Shinhyeok Oh, Hyojun Go, Hyeongdon Moon, Yunsung Lee, Myeongho Jeong, Hyun Seung Lee, Seungtaek Choi
| Challenge: | Existing evaluations of QG methods rely on single reference-based similarity metrics . multiple (pseudo) references are more effective for QG evaluation . |
| Approach: | They propose to paraphrase the reference question for a more robust QG evaluation. |
| Outcome: | The proposed frameworks show higher correlation with human evaluations than evaluation with a single reference. |
Evaluating the Knowledge Dependency of Questions (2022.emnlp-main)
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Hyeongdon Moon, Yoonseok Yang, Hangyeol Yu, Seunghyun Lee, Myeongho Jeong, Juneyoung Park, Jamin Shin, Minsam Kim, Seungtaek Choi
| Challenge: | Existing evaluation metrics for MCQ generation focus on the n-gram based similarity of the generated MCq to the gold sample and disregard their educational value. |
| Approach: | They propose to use a human survey to measure the MCQ’s answerability given knowledge of the target fact. |
| Outcome: | The proposed methods measure the MCQ’s answerability given knowledge of the target fact. |
On Complementarity Objectives for Hybrid Retrieval (2023.acl-long)
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| Challenge: | Existing approaches to hybrid retrieval focus on sparse models to capture “residual” features neglected in spars. |
| Approach: | They propose a new objective to capture a fuller notion of complementarity . they propose to improve the model's Ratio of Complementarity to improve RoC . |
| Outcome: | The proposed method outperforms state-of-the-art methods on three representative IR benchmarks with statistical significance. |
Retrieval-Augmented Controllable Review Generation (2020.coling-main)
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| Challenge: | Existing approaches to generate reviews using attribute identifiers are limited and dependent on how well they can capture vector representations of attributes. |
| Approach: | They propose to leverage attributes as inputs for review generation by using reference sets . they propose to use these references to enrich inductive biases of given attributes . |
| Outcome: | The proposed model improves over previous approaches on automatic and human evaluation metrics. |
Debiasing Event Understanding for Visual Commonsense Tasks (2022.findings-acl)
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| Challenge: | a recent study shows that object-based event understanding is purely likelihood-based, leading to incorrect event prediction. |
| Approach: | They propose to mitigate object-based event understanding by optimizing aggregation with association-based prediction. |
| Outcome: | The proposed approach improves visual commonsense reasoning tasks by combining do-calculus with association-based prediction. |
Retrieval-augmented Video Encoding for Instructional Captioning (2023.findings-acl)
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| Challenge: | Instructional videos provide a detailed multimodal context of each procedure in instruction. key-object degeneracy is a problem for machine systems, causing incorrect captions. |
| Approach: | They propose a retrieval-based framework to augment the model representations in the presence of key-object degeneracy. |
| Outcome: | The proposed framework can be extended over baselines using modalities with key-object degeneracy. |
Visual Choice of Plausible Alternatives: An Evaluation of Image-based Commonsense Causal Reasoning (L18-1)
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Jinyoung Yeo, Gyeongbok Lee, Gengyu Wang, Seungtaek Choi, Hyunsouk Cho, Reinald Kim Amplayo, Seung-won Hwang
| Challenge: | Existing methods for evaluating plausibility of events are focused on measuring causal dependency between events or actions. |
| Approach: | They propose a task to identify the more plausible alternative with their commonsense causal context. |
| Outcome: | The proposed task is based on a visual COPA dataset with 380 questions and over 1K images with various topics. |
Interventional Speech Noise Injection for ASR Generalizable Spoken Language Understanding (2024.emnlp-main)
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| Challenge: | Existing methods to increase the robustness of pre-trained language models (PLMs) against unseen ASR systems produce noisy inputs for SLU models, which can significantly degrade their performance. |
| Approach: | They propose to introduce ASR-plausible noises into pre-trained language models by cutting off the non-causal effect of noises. |
| Outcome: | The proposed method improves the robustness and generalizability of SLU models against unseen ASR systems by cutting off the non-causal effect of noises. |
MICRON: Multigranular Interaction for Contextualizing RepresentatiON in Non-factoid Question Answering (D19-1)
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| Challenge: | Existing approaches for non-factoid question answering can be categorized into representation and interaction focused approaches. |
| Approach: | They propose a novel approach which derives contextualized uni-gram representation from n-grams. |
| Outcome: | The proposed approach achieves state-of-the-art in two public non-factoid question answering datasets. |
Less is More: Attention Supervision with Counterfactuals for Text Classification (2020.emnlp-main)
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| Challenge: | Specifically, we explore the advantage of counterfactual reasoning, over associative reasoning . Adding human supervision to attention has been shown to improve model predictions and explanations . |
| Approach: | They propose to use machine-augmented human attention supervision to enhance model quality. |
| Outcome: | The proposed method is more effective than existing methods requiring higher annotation cost . the proposed method can be trained to generate similar attention to human supervision . |
Structure-Augmented Keyphrase Generation (2021.emnlp-main)
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| Challenge: | Creating keyphrases that are likely to be words absent from the given document is challenging . |
| Approach: | They propose novel keyphrase generation tasks that augment missing context by adding keyphrases to documents. |
| Outcome: | The proposed keyphrase generation task outperforms the state-of-the-art in two keyphrase tasks. |
FLEX: Expert-level False-Less EXecution Metric for Text-to-SQL Benchmark (2025.naacl-long)
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| Challenge: | Existing evaluation methods for text-to-SQL systems show many false positives and negatives . however, the Execution Accuracy (EX) metric is flawed and can diverge from human experts. |
| Approach: | They propose a method to evaluate text-to-SQL systems using large language models to emulate human expert-level evaluation of SQL queries. |
| Outcome: | The proposed metric improves agreement with human experts with comprehensive context and sophisticated criteria. |
Towards Compositional Generalization in Code Search (2022.emnlp-main)
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| Challenge: | Existing code search models that focus on code as an unstructured sequence fail to generalize when the lexical perturbation without changing structures and labels is applied in test codes. |
| Approach: | They propose a compositional generalization model that extracts structural elements and a code template that targets compositional genericization. |
| Outcome: | The proposed model is complementary to flow graphs in GraphCodeBERT, by enhancing structural context around variables. |
Cross Encoding as Augmentation: Towards Effective Educational Text Classification (2023.findings-acl)
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Hyun Seung Lee, Seungtaek Choi, Yunsung Lee, Hyeongdon Moon, Shinhyeok Oh, Myeongho Jeong, Hyojun Go, Christian Wallraven
| Challenge: | Existing methods to improve text classification in education suffer from data scarcity . authors propose a retrieval approach that provides effective learning in educational text classification. |
| Approach: | They propose a retrieval approach that provides effective learning in educational text classification by introducing cross-encoder style texts to a bi-encoding architecture. |
| Outcome: | The proposed method is effective in multi-label scenarios and low-resource tags compared to state-of-the-art models. |