Papers with supervised counterparts
Syntactic and Semantic-driven Learning for Open Information Extraction (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Experimental results show that our approach significantly outperforms the supervised counterparts, and can even achieve competitive performance to supervised state-of-the-art (SoA) model. |
| Approach: | They propose a syntactic and semantic-driven learning approach that can learn open IE models without human-labelled data by leveraging syntakic and semantic knowledge as noisier, higher-level supervision. |
| Outcome: | The proposed approach outperforms supervised counterparts and can achieve competitive performance to supervised state-of-the-art models. |
Unsupervised Recurrent Neural Network Grammars (N19-1)
Copied to clipboard
| Challenge: | RNNGs model syntax and structure by incrementally generating a syntax tree and sentence in a top-down, left-to-right order. |
| Approach: | They explore unsupervised learning of recurrent neural network grammars for language modeling and grammar induction. |
| Outcome: | The proposed model outperforms standard sequential language models and improves parsing performance. |
Improved Latent Tree Induction with Distant Supervision via Span Constraints (2021.emnlp-main)
Copied to clipboard
Zhiyang Xu, Andrew Drozdov, Jay Yoon Lee, Tim O’Gorman, Subendhu Rongali, Dylan Finkbeiner, Shilpa Suresh, Mohit Iyyer, Andrew McCallum
| Challenge: | Distant supervision is not a practical way to perform unsupervised syntactic parsing. |
| Approach: | They propose a technique that uses distant supervision to improve unsupervised constituency parsing by using phrase bracketing. |
| Outcome: | The proposed method improves constituency parsing on English WSJ Penn Treebank by more than 5 F1 compared with full parse tree annotations. |
Unsupervised Statistical Machine Translation (D18-1)
Copied to clipboard
| Challenge: | Neural Machine Translation (NMT) systems can be trained from monolingual corpora without supervision. |
| Approach: | They propose a phrase-based approach that trains from monolingual corpora . their method is based on phrase-driven Statistical Machine Translation (SMT) they propose to train NMT systems without supervision from monolinguistic corpors . |
| Outcome: | The proposed approach improves on the existing supervised systems by combining a phrase table with an n-gram language model and fine-tuning hyperparameters through an unsupervised MERT variant. |
Adversarial Self-Supervised Learning for Out-of-Domain Detection (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing methods for detecting out-of-domain (OOD) intents are unsupervised and require extensive labeled data. |
| Approach: | They propose a self-supervised contrastive learning framework to model discriminative semantic features from unlabeled data. |
| Outcome: | The proposed framework outperforms baseline methods on two public benchmark datasets with a statistically significant margin. |
Cross Domain Classification of Education Talk Turns (2025.coling-main)
Copied to clipboard
| Challenge: | Prior research has focused on the annotation of conversational talk-turns within the classroom, offering a statistical analysis of the various types of discourse prevalent in these environments. |
| Approach: | They examine the generalizability and transferability of text classifiers trained to predict classroom discourse across educational domains by accompanying each talk turn with dialog-level context. |
| Outcome: | The proposed models exhibit high generalizability when training and test datasets originate from the same or similar domains. |
Unsupervised Summarization Re-ranking (2023.findings-acl)
Copied to clipboard
| 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. |
SimCSE: Simple Contrastive Learning of Sentence Embeddings (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for learning universal sentence embeddings are based on unsupervised approaches with only dropout as noise. |
| Approach: | They propose an unsupervised approach that takes an input sentence and predicts itself in a contrastive objective with only standard dropout used as noise. |
| Outcome: | The proposed framework performs on par with previous supervised approaches and can produce superior sentence embeddings from unlabeled or labeled data. |
Generate, Discriminate and Contrast: A Semi-Supervised Sentence Representation Learning Framework (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing supervised sentence embedding techniques rely on expensive human-annotated sentence pairs as the supervised signals. |
| Approach: | They propose a semi-supervised sentence embedding framework that leverages large-scale unlabeled data. |
| Outcome: | The proposed framework surpasses state-of-the-art methods on four domain adaptation tasks. |