Papers by Qing Ling
Self Question-answering: Aspect-based Sentiment Analysis by Role Flipped Machine Reading Comprehension (2021.findings-emnlp)
Copied to clipboard
| Challenge: | unified Aspect-based Sentiment Analysis (ABSA) aims to couple aspect terms with their corresponding opinion terms, which might make it easier to predict sentiment polarities. |
| Approach: | They propose a new paradigm to pair aspect terms with their corresponding opinion terms . they propose to use a machine learning paradigm to solve the unified ABSA task . |
| Outcome: | The proposed framework can solve the ABSA task without any additional data annotation or transformation. |
Conditional Augmentation for Aspect Term Extraction via Masked Sequence-to-Sequence Generation (2020.acl-main)
Copied to clipboard
| Challenge: | Aspect term extraction is a task to extract aspect terms from review texts as opinion targets for sentiment analysis. |
| Approach: | They propose a conditional generation task for augmentation of aspect term extraction . they use a sequence-to-sequence method that generates a new sentence . results confirm that their method alleviates the data scarcity problem significantly . |
| Outcome: | The proposed method reduces the data scarcity problem significantly and boosts current models. |
VAEGPT-Sim: Improving Sentence Representation with Limited Corpus Using Gradually-Denoising VAE (2024.findings-acl)
Copied to clipboard
| Challenge: | Text embedding requires a highly efficient method for training domain-specific models on limited corpora. |
| Approach: | They propose a model that combines a denoising variational autoencoder with a target-specific discriminator to generate synonymous sentences that closely resemble human language. |
| Outcome: | The proposed model surpasses ConSERT by 2.8 points in small-dataset training on STS benchmarks. |
Reading Like HER: Human Reading Inspired Extractive Summarization (D19-1)
Copied to clipboard
| Challenge: | Existing methods for extracting text summarization are abstractive and extractive. |
| Approach: | They propose a novel approach for extractive summarization by simulating two stages . they adopt a convolutional neural network to encode gist of paragraphs for rough reading . |
| Outcome: | The proposed method significantly outperforms the state-of-the-art extractive methods on CNN and DailyMail datasets. |
Making Flexible Use of Subtasks: A Multiplex Interaction Network for Unified Aspect-based Sentiment Analysis (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing studies aim to integrate multiple sub-tasks into a unified ABSA model but suffer from major disadvantages . |
| Approach: | They propose a multi-task learning approach to make use of sub-tasks for a unified ABSA. |
| Outcome: | The proposed model can work well when some sub-tasks are absent, and the interactive relations among subtasks not adequate. |
PENS: A Dataset and Generic Framework for Personalized News Headline Generation (2021.acl-long)
Copied to clipboard
| Challenge: | Using a dataset of Microsoft News, we propose a generic framework to personalize a text generator and establish personalized headlines. |
| Approach: | They propose a generic framework to personalize a news headline generator and establish personalized headlines by leveraging user behavioral data. |
| Outcome: | The proposed framework is based on user preference data and user preference injections to personalize a text generator and establish personalized headlines. |