Papers with distant supervision approach
Clinical Contradiction Detection (2023.emnlp-main)
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| Challenge: | Detecting contradictions in text is difficult since it requires clinical expertise. |
| Approach: | They propose to use a medical ontology to build a seed of potential medical contradictions in medical abstracts by distant supervision. |
| Outcome: | The proposed method weakly supervises state-of-the-art deep learning models and shows significant improvements across multiple medical contradiction datasets. |
Sentiment-Stance-Specificity (SSS) Dataset: Identifying Support-based Entailment among Opinions. (L18-1)
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| Challenge: | Argument mining is a method for extracting argument components and structures from natural language texts. |
| Approach: | They propose to model arguments as a set of premises that either support each other or collectively support a conclusion. |
| Outcome: | The proposed rules give an overall accuracy of 0.83 for the three datasets. |
Interpreting Indirect Answers to Yes-No Questions in Multiple Languages (2023.findings-emnlp)
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Zijie Wang, Md Hossain, Shivam Mathur, Terry Melo, Kadir Ozler, Keun Park, Jacob Quintero, MohammadHossein Rezaei, Shreya Shakya, Md Uddin, Eduardo Blanco
| Challenge: | Existing models for Yes-no questions skip polar keywords and instead use long explanations that must be interpreted. |
| Approach: | They propose a distant supervision approach to collect training data and show that direct answers are useful to train models to interpret indirect answers. |
| Outcome: | The proposed model achieves a 68% to 76% F1-score on multilingual Question-Answering benchmarks. |
UniRPG: Unified Discrete Reasoning over Table and Text as Program Generation (2022.emnlp-main)
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| Challenge: | Existing methods for question answering using knowledge resources are mixed-of-experts and semantic parsing-based. |
| Approach: | They propose a semantic-parsing-based approach to perform Unified discrete Reasoning over heterogeneous knowledge resources as Program Generation. |
| Outcome: | The proposed approach improves interpretability and scalability over table and text . it achieves promising performance on the TAT-QA dataset without annotation . |