Papers by Seunghak Yu
Cut to the Chase: A Context Zoom-in Network for Reading Comprehension (D18-1)
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| Challenge: | Recent deep-learning based models suffer from reasoning over long documents and do not trivially generalize to cases where the answer is not present as a span. |
| Approach: | They propose a novel context zoom-in network (ConZNet) that can skip through irrelevant parts of a document and generate an answer using only the relevant regions of text. |
| Outcome: | The proposed architecture outperforms state-of-the-art results by 12.62% (ROUGE-L) relative improvement on the recently proposed and challenging RC dataset ‘NarrativeQA’. |
Fine-Grained Analysis of Propaganda in News Article (D19-1)
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| Challenge: | Existing methods for detecting propaganda are noisy and lack of explainability. |
| Approach: | They propose to perform fine-grained analysis of texts by detecting all fragments that contain propaganda techniques as well as their type. |
| Outcome: | The proposed model outperforms several strong BERT-based baselines. |
Large-scale Lifelong Learning of In-context Instructions and How to Tackle It (2023.acl-long)
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| Challenge: | In-context instruction learning is a method to improve the target PLM’s instance- and task-level generalization performance as it observes more tasks. |
| Approach: | They propose to fine-tune a Pre-trained Language Model (PLM) on a set of tasks with in-context instructions and to extend this property to a scenario in which tasks are fed to the target PLM in a sequential manner. |
| Outcome: | The proposed method achieves noticeable improvements in both types of generalization, nearly reaching the upper bound performance obtained through joint training. |
Prta: A System to Support the Analysis of Propaganda Techniques in the News (2020.acl-demos)
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Giovanni Da San Martino, Shaden Shaar, Yifan Zhang, Seunghak Yu, Alberto Barrón-Cedeño, Preslav Nakov
| Challenge: | recent events have brought the public attention to the dangers of online disinformation. |
| Approach: | a new tool helps users analyze propaganda using specific rhetorical and psychological techniques. a prta system identifies the spans in which propaganda techniques occur and compares them. |
| Outcome: | a new tool can analyze articles crawled on a regular basis and compare them on the basis of their use of propaganda techniques. |
Supervised Clustering of Questions into Intents for Dialog System Applications (D18-1)
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| Challenge: | Existing methods for detecting intents in text are task-specific and costly . current methods focus on manually analyzing user questions and creating a taxonomy of intents to be attached to the appropriate actions. |
| Approach: | They propose a model for automatically clustering questions into user intents to help design tasks . they use powerful semantic classifiers and supervised clustering methods based on structured output . |
| Outcome: | The proposed model improves on two intent clustering corpora on two languages/domains. |
MemoReader: Large-Scale Reading Comprehension through Neural Memory Controller (D18-1)
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| Challenge: | Existing approaches to machine reading comprehension are limited in understanding, up to a few paragraphs, failing to comprehend lengthy documents. |
| Approach: | They propose a deep neural network architecture to handle a long-range dependency in RC tasks. |
| Outcome: | The proposed method outperforms existing methods especially for lengthy documents. |
On-Device Neural Language Model Based Word Prediction (C18-2)
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| Challenge: | Currently, on-device keyboards have limited memory and response time for word prediction . a proposed on-device neural language model based word prediction method is available for mobile devices . |
| Approach: | They propose an on-device neural language model based word prediction method that optimizes run-time memory and provides a real-time prediction environment. |
| Outcome: | The proposed model outperforms existing methods for word prediction in keystroke savings and word prediction rate and has been commercialized. |
Cooperative Self-training of Machine Reading Comprehension (2022.naacl-main)
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| Challenge: | Pretrained language models provide high-quality contextualized word embeddings, but training question answering models requires large amounts of annotated data for specific domains. |
| Approach: | They propose a framework for automatically generating more non-trivial question-answer pairs to improve model performance. |
| Outcome: | The proposed framework outperforms state-of-the-art (SOTA) pretrained language models and transfer learning approaches on standard question-answering benchmarks. |