Papers by Sinno Pan
SOUL: Towards Sentiment and Opinion Understanding of Language (2023.emnlp-main)
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| Challenge: | Sentiment analysis models often fail to capture the broader complexities of sentiment analysis. |
| Approach: | They propose a task to evaluate sentiment understanding through two subtasks . they annotate a new dataset comprising 15,028 statements from 3,638 reviews . |
| Outcome: | The proposed task evaluates sentiment understanding through two subtasks . it is a challenging task for both small and large language models, with performance gaps of up to 27% . |
Domain Confused Contrastive Learning for Unsupervised Domain Adaptation (2022.naacl-main)
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| Challenge: | Existing studies on domain-shifting adaptations have focused on domain . |
| Approach: | They propose a self-supervised approach to unsupervised domain adduction using domain puzzles to bridge the source and target domains and retain discriminative representations after adaptation. |
| Outcome: | The proposed approach outperforms baselines and further ablation studies show that it is more stable and effective when performing other data augmentations. |
Sentiment Analysis in the Era of Large Language Models: A Reality Check (2024.findings-naacl)
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| Challenge: | Sentiment analysis (SA) has been a long-standing research area in natural language processing. |
| Approach: | They propose a benchmark to evaluate LLMs' SA abilities and propose 'sentiEval' benchmark to be used for a more comprehensive evaluation. |
| Outcome: | The proposed benchmark outperforms small language models on 26 datasets on 13 tasks and compared them with LLMs trained on domain-specific datasets. |
Deep Inductive Logic Reasoning for Multi-Hop Reading Comprehension (2022.acl-long)
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| Challenge: | a new method for multi-hop reading comprehension uses deep learning to reason across documents . a previous study showed that deep learning methods only implicitly encode query-related information into embeddings . |
| Approach: | They propose a deep-learning based inductive logic reasoning method that extracts query-related information and conducts logic reasoning among filtered information. |
| Outcome: | The proposed model is evaluated on two reading comprehension datasets . it uses attentive memories with novel differentiable logic operators . |
Adapt in Contexts: Retrieval-Augmented Domain Adaptation via In-Context Learning (2023.emnlp-main)
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| Challenge: | Large language models have demonstrated their capability with few-shot inference . however, in-domain demonstrations are not always available in real scenarios . |
| Approach: | They propose unsupervised domain adaptation problem to adapt language models from source domain to target domain without any target labels. |
| Outcome: | The proposed model performs better than baseline models on Sentiment Analysis and Named Entity Recognition tasks. |