Papers with supervised setup
Reinforcement-based denoising of distantly supervised NER with partial annotation (D19-61)
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| Challenge: | Existing named entity recognition systems rely on large amounts of human-labeled data for supervision, but the result is noisy. |
| Approach: | They propose to use partial annotation to address false negative cases and implement a reinforcement learning strategy to identify false positive instances. |
| Outcome: | The proposed model reduces the amount of manually annotated data required to perform NER in a new domain. |
Reasoning Implicit Sentiment with Chain-of-Thought Prompting (2023.acl-short)
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| Challenge: | In implicit sentiment analysis, the opinion cues come in an implicit and obscure manner. |
| Approach: | They propose a three-step prompting principle for THOR to step-by-step induce the implicit aspect, opinion and finally the sentiment polarity. |
| Outcome: | The proposed framework pushes the state-of-the-art (SoTA) by over 6% F1 on supervised setup and more strikingly, boosts the SoTA by over 50% F1 with THOR+GPT3. |
D2U: Distance-to-Uniform Learning for Out-of-Scope Detection (2022.naacl-main)
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| Challenge: | Existing methods for out-of-scope (OOS) detection use classifier confidence score, but model cannot infer correctly. |
| Approach: | They propose a zero-shot post-processing step that exploits the classification confidence score and the shape of the entire output distribution. |
| Outcome: | The proposed method improves performance when there is no OOS training data and learning procedure when OOS data is available. |
Unsupervised Summarization Re-ranking (2023.findings-acl)
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| Challenge: | Abstractive summarization models have been gaining popularity, but performance of unsupervised models still lags behind supervised models. |
| Approach: | They propose to re-rank summary candidates in an unsupervised manner to close the performance gap between unsupervised and supervised models. |
| Outcome: | The proposed model improves unsupervised models by up to 7.27% and ChatGPT by up 6.86% relative mean ROUGE across four widely-adopted summarization benchmarks. |
Paraphrase Generation as Unsupervised Machine Translation (2022.coling-1)
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| Challenge: | Existing methods for paraphrase generation rely on labeled datasets or are limited in narrow domains. |
| Approach: | They propose a paradigm for paraphrase generation by treating the task as unsupervised machine translation based on pairs of unlabeled monolingual sentences. |
| Outcome: | The proposed paradigm can generate paraphrases on a large unlabeled monolingual corpus without relying on bilingual sentence pairs. |