Papers by Gary Geunbae Lee
Explainable Multi-hop Question Generation: An End-to-End Approach without Intermediate Question Labeling (2024.lrec-main)
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| Challenge: | Existing models that generate complex questions do not explain reasoning process behind generated multi-hop questions. |
| Approach: | They propose an end-to-end question rewriting model that increases question complexity through sequential rewrite. |
| Outcome: | The proposed model generates complex questions that require multi-step reasoning over multiple documents. |
Out-of-domain Detection based on Generative Adversarial Network (D18-1)
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| Challenge: | Existing methods for out-of-domain (OOD) detection require huge effort to collect OOD sentences. |
| Approach: | They propose to use only in-domain (IND) sentences to build a generative adversarial network (GAN) of which the discriminator generates low scores for OOD sentences. |
| Outcome: | The proposed method is most accurate compared to existing methods on multi-domain dialog systems. |
Conversational QA Dataset Generation with Answer Revision (2022.coling-1)
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| Challenge: | Existing frameworks for conversational question-answer generation generate a large-scale dataset based on input passages. |
| Approach: | They propose a conversational question-answer generation framework that extracts question-worthy phrases from passages and generates corresponding questions considering previous conversations. |
| Outcome: | The proposed framework improves the quality of synthetic data and can be used for domain adaptation of conversational question answering. |
Leveraging the Interplay between Syntactic and Acoustic Cues for Optimizing Korean TTS Pause Formation (2024.lrec-main)
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| Challenge: | despite recent advances in speech synthesis, the focus of research has been on high-resource languages like English. |
| Approach: | They propose a framework that incorporates modeling of syntactic and acoustic cues associated with pausing patterns. |
| Outcome: | The proposed framework generates natural speech even for longer and intricate out-of-domain sentences, despite training on short audio clips. |
Denoising Table-Text Retrieval for Open-Domain Question Answering (2024.lrec-main)
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| Challenge: | Existing studies in table-text open-domain question answering have problems with false-positive labels in training datasets. |
| Approach: | They propose a denoised table-text retriever that discards false positives from training datasets . they integrate table-level ranking information into the retriever to assist in finding evidence . |
| Outcome: | The proposed method outperforms baselines on retrieval recall and QA tasks. |
Schema Encoding for Transferable Dialogue State Tracking (2022.coling-1)
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| Challenge: | Recent work has focused on deep neural models for task-oriented dialogue systems . however, the neural models require a large dataset for training and a new dataset to be trained on another domain. |
| Approach: | They propose a schema encoder for transferable dialogue state tracking to new domains . they aim to transfer the model to new datasets by encoding new schemas based on the dataset . |
| Outcome: | The proposed method improves the accuracy of the proposed model on multi-domain settings. |
Prompt- and Trait Relation-aware Cross-prompt Essay Trait Scoring (2023.findings-acl)
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| Challenge: | Existing systems assume to grade essays on same prompt as used in training and assign only a holistic score. |
| Approach: | They propose a prompt- and trait relation-aware cross-prompt essay trait scorer that encodes prompt-awful essay representation by essay-promotion attention and utilizing the topic-coherence feature extracted by the topic model. |
| Outcome: | The proposed model shows state-of-the-art results for all prompts and traits. |
Multi-Type Conversational Question-Answer Generation with Closed-ended and Unanswerable Questions (2022.aacl-short)
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| Challenge: | Conversational question answering (CQA) aims to answer a question based on a given passage and previous conversation. |
| Approach: | They propose a method to synthesize data for CQA with various question types . they propose 'hierarchical answerability classification' module that improves quality of synthetic data while acquiring unanswerable questions. |
| Outcome: | The proposed framework improves quality of synthetic data while acquiring unanswerable questions. |