Papers with unsupervised techniques
Hybrid Models for Aspects Extraction without Labelled Dataset (D19-66)
Copied to clipboard
| Challenge: | Existing methods to extract aspects from opinions focus on explicit aspects, but sentences do not state them explicitly. |
| Approach: | They propose to use a dictionary-based approach to identify and extract aspects from opinions . they propose to combine topic modelling and dictionary--based method . |
| Outcome: | The proposed models outperform baseline topic model and dictionary-based approach in 58.70% of the evaluations. |
QuickGraph: A Rapid Annotation Tool for Knowledge Graph Extraction from Technical Text (2022.acl-demo)
Copied to clipboard
| Challenge: | Acquiring high-quality annotated corpora for complex multi-task information extraction (MT-IE) is an arduous and costly process for human-annotators. |
| Approach: | They propose a supervised MT-IE annotation tool built with indirect weak supervision and clustering to maximise annotator productivity. |
| Outcome: | The proposed tool is compared with existing tools in the field of MT-IE and aims to increase annotator productivity. |
A Survey on Recent Advances in Keyphrase Extraction from Pre-trained Language Models (2023.findings-eacl)
Copied to clipboard
| Challenge: | Keyphrase extraction is a key component in Natural Language Processing (NLP) systems for selecting a set of phrases from the document that could summarize the important information discussed in the source document. |
| Approach: | They propose to use supervised and unsupervised keyphrase extraction techniques to investigate the state-of-the-art models for keyphrase extracting. |
| Outcome: | The proposed keyphrase extraction system can significantly accelerate the speed of retrieval and help people get first-hand information from a long document quickly and accurately. |
Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts (2024.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) are known to perform tasks by simply observing few exemplars, but performance among under-represented languages falls behind due to pre-training data imbalance. |
| Approach: | They propose to assemble synthetic exemplars from high-resource languages to prompt LLMs to translate from any language into English and use them to create intra-lingual exemplar models to perform tasks in target languages. |
| Outcome: | The proposed method outperforms supervised few-shot learning in LLMs of different sizes for translations between English and 13 Indic and 21 African low-resource languages. |
Multi-Input Attention for Unsupervised OCR Correction (P18-1)
Copied to clipboard
| Challenge: | Existing methods for OCR correction are mostly supervised methods that correct recognition errors in a single output. |
| Approach: | They propose a sequence-to-sequence model with attention and a decoder with attention averaging to search for consensus among multiple sequences. |
| Outcome: | The proposed methods cut the character and word error rates nearly in half on single inputs and can rival supervised methods. |
Translating Translationese: A Two-Step Approach to Unsupervised Machine Translation (P19-1)
Copied to clipboard
| Challenge: | Using a dictionary, given a rough, target language natives can uncover the latent, fully-fluent rendering of the translation. |
| Approach: | They propose a method that breaks translation into two steps by generating a dictionary and then ‘translating’ the resulting pseudo-translation into a fully fluent translation. |
| Outcome: | The proposed method 'gets better translation results on high-resource languages than previously published unsupervised MT studies' |
Interactive Concept Learning for Uncovering Latent Themes in Large Text Collections (2023.findings-acl)
Copied to clipboard
| Challenge: | Topic modeling is a popular method for identifying emerging themes from text collections. |
| Approach: | They propose a framework that receives and encodes expert feedback at different levels of abstraction. |
| Outcome: | The proposed framework combines automation and manual coding, allowing experts to maintain control while reducing the manual effort required. |
Unsupervised Machine Translation in Real-World Scenarios (2022.lrec-1)
Copied to clipboard
Ona de Gibert Bonet, Iakes Goenaga, Jordi Armengol-Estapé, Olatz Perez-de-Viñaspre, Carla Parra Escartín, Marina Sanchez, Mārcis Pinnis, Gorka Labaka, Maite Melero
| Challenge: | a recent study has shown that unsupervised methods rely on monolingual corpora to build MT systems. |
| Approach: | They present the results of the MT4All CEF project using monolingual corpora . they propose to generate bilingual dictionaries and translation models from monolingual data . |
| Outcome: | The proposed method generates bilingual dictionaries and translation models from monolingual corpora . results show that it is comparable to general domain supervised translation . |
Adapting Unsupervised Syntactic Parsing Methodology for Discourse Dependency Parsing (2021.acl-long)
Copied to clipboard
| Challenge: | Discourse dependency parsing is a task that requires a large amount of training data, but there is little research on it. |
| Approach: | They propose to adapt unsupervised syntactic dependency parsing methods for unsupervised discourse dependency parses using unlabeled training data. |
| Outcome: | The proposed methods outperform existing methods in semi-supervised and supervised settings and outperformed existing methods. |