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Found 12 papers, 3 papers with code
Progressive Self-Training with Discriminator for Aspect Term Extraction
In this paper, we use two means to alleviate the noise in the pseudo-labels.
Affective-NLI: Towards Accurate and Interpretable Personality Recognition in Conversation
To utilize affectivity within dialog content for accurate personality recognition, we fine-tuned a pre-trained language model specifically for emotion recognition in conversations, facilitating real-time affective annotations for utterances.
Personality-affected Emotion Generation in Dialog Systems
Therefore, we propose a new task, Personality-affected Emotion Generation, to generate emotion based on the personality given to the dialog system and further investigate a solution through the personality-affected mood transition.
Low-Resource Court Judgment Summarization for Common Law Systems
Current summarization datasets are insufficient to satisfy the demands of summarizing precedents across multiple jurisdictions, especially when labeled data are scarce for many jurisdictions.
Generating a Structured Summary of Numerous Academic Papers: Dataset and Method
Existing MDS datasets usually focus on producing the structureless summary covering a few input documents.
Long Text and Multi-Table Summarization: Dataset and Method
Within a report document, the salient information can be scattered in the textual and non-textual content.
Enhancing Automated Essay Scoring Performance via Fine-tuning Pre-trained Language Models with Combination of Regression and Ranking
However, to solve the AES task, previous works utilize shallow neural networks to learn essay representations and constrain calculated scores with regression loss or ranking loss, respectively.
Emerging App Issue Identification via Online Joint Sentiment-Topic Tracing
Experiments on popular apps from Google Play and Apple's App Store demonstrate the effectiveness of MERIT in identifying emerging app issues, improving the state-of-the-art method by 22. 3% in terms of F1-score.
EPARS: Early Prediction of At-risk Students with Online and Offline Learning Behaviors
We propose a novel algorithm (EPARS) that could early predict STAR in a semester by modeling online and offline learning behaviors.
Decode with Template: Content Preserving Sentiment Transfer
The two major challenges in existing works lie in (1) effectively disentangling the original sentiment from input sentences; and (2) preserving the semantic content while transferring the sentiment.
GGP: Glossary Guided Post-processing for Word Embedding Learning
To enhance corpus based word embedding models, researchers utilize domain knowledge to learn more distinguishable representations via joint optimization and post-processing based models.
