Papers with supervised learning approach
Supervised Open Information Extraction (N18-1)
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| Challenge: | Existing methods for Open Information Extraction (Open IE) use semisupervised approaches or rule-based algorithms. |
| Approach: | They propose a supervised approach to Open Information Extraction (Open IE) they build on recent deep Semantic Role Labeling models to extract Open IE tuples . |
| Outcome: | The proposed model outperforms state-of-the-art Open IE systems on benchmark datasets. |
Disambiguation of Verbal Shifters (L18-1)
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| Challenge: | Negation is a contextual phenomenon that needs to be addressed in sentiment analysis. |
| Approach: | They propose a supervised learning approach to disambiguate verbal shifters using generalization features and a new lexicon. |
| Outcome: | The proposed approach takes into account various features, particularly generalization features. |
Translation-based Supervision for Policy Generation in Simultaneous Neural Machine Translation (2021.emnlp-main)
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| Challenge: | Existing approaches to train simultaneous machine translation agents have been used to find the optimal action sequences for translation quality and lag. |
| Approach: | They propose a supervised learning approach that detects minimum reads required for generating target tokens by comparing simultaneous translations against full-sentence translations. |
| Outcome: | The proposed method produces much higher quality translations while minimizing the average lag in simultaneous translation. |
Unsupervised Task Graph Generation from Instructional Video Transcripts (2023.findings-acl)
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| Challenge: | Existing approaches to task graph generation use instruction-tuned language models to generate accurate task graphs. |
| Approach: | They propose an unsupervised approach that combines the reasoning capabilities of instruction-tuned language models with clustering and ranking components to generate accurate task graphs. |
| Outcome: | The proposed approach generates more accurate task graphs than a supervised learning approach on tasks from the ProceL and CrossTask datasets. |
Ideology Prediction from Scarce and Biased Supervision: Learn to Disregard the “What” and Focus on the “How”! (2023.acl-long)
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| Challenge: | a novel supervised learning approach for political ideology prediction is needed for many applications. |
| Approach: | They propose a supervised learning approach for political ideology prediction that decomposes document embeddings into a linear superposition of two vectors. |
| Outcome: | The proposed model outperforms state-of-the-art models on two benchmark datasets with biased data with 5% accuracy. |
Cross-Sentence N-ary Relation Extraction using Lower-Arity Universal Schemas (D19-1)
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| Challenge: | Existing approaches to extract n-ary relations from text are limited to binary relations. |
| Approach: | They propose to learn relation representations of lower-arity facts from decomposing higher-arities . they conduct experiments with datasets for ternary relation extraction . |
| Outcome: | The proposed method improves the performance of n-ary relation extraction methods compared to previous methods. |