Papers by Jyun-Yu Jiang
MinPrompt: Graph-based Minimal Prompt Data Augmentation for Few-shot Question Answering (2024.acl-long)
Copied to clipboard
| Challenge: | Recent advances in few-shot question answering rely on pre-trained large language models and fine-tuning in specific settings. |
| Approach: | They propose to select the most informative data for fine-tuning to improve efficiency . they use an approximate graph algorithm and unsupervised question generation to generate QA pairs . |
| Outcome: | The proposed framework improves the performance of the few-shot question answering task on the open-domain QA task. |
Learning to Discriminate Perturbations for Blocking Adversarial Attacks in Text Classification (D19-1)
Copied to clipboard
| Challenge: | Existing studies on adversarial attacks on deep learning models focus on generation of adversarials and defense against adversarial attacks. |
| Approach: | They propose a framework to identify and adjust malicious perturbations and block adversarial attacks for machine learning models. |
| Outcome: | The proposed framework outperforms baseline methods in blocking adversarial attacks for text classification models. |
Long Document Ranking with Query-Directed Sparse Transformer (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to document ranking require long documents to be broken to fit in pretrained models. |
| Approach: | They propose a Query-Directed Sparse attention model that induces IR-axiomatic structures in transformer self-attention. |
| Outcome: | The proposed model enforces the principle properties desired in ranking while also enjoying efficiency from sparsity. |
Enhancing Air Quality Prediction with Social Media and Natural Language Processing (P19-1)
Copied to clipboard
| Challenge: | predicting air quality is a major concern for human health, but the changes of air quality conditions are still difficult to monitor. |
| Approach: | They propose to exploit social media and natural language processing techniques to enhance air quality prediction. |
| Outcome: | The proposed approach improves air quality prediction over baseline that does not use social media by 6.9% to 17.7% in macro-F1 scores. |
Learning to Disentangle Interleaved Conversational Threads with a Siamese Hierarchical Network and Similarity Ranking (N18-1)
Copied to clipboard
| Challenge: | Existing methods to disentangle interleaved conversations can lead to difficulties in following discussions and retrieving relevant information from simultaneous messages. |
| Approach: | They propose to leverage representation learning to separate intermingled messages into detached conversations by estimating conversation-level similarity between closely posted messages. |
| Outcome: | The proposed approach outperforms baselines in pairwise similarity estimation and conversation disentanglement. |
“The Boating Store Had Its Best Sail Ever”: Pronunciation-attentive Contextualized Pun Recognition (2020.acl-main)
Copied to clipboard
| Challenge: | Identifying and modeling puns is challenging as they involve implicit semantic or phonological tricks. |
| Approach: | They propose a method to detect puns in a sentence and then locate them in it . they propose to capture phonetic associations between the context and phonetic symbols . |
| Outcome: | The proposed method outperforms state-of-the-art methods in pun detection and location tasks. |