Papers with unsupervised algorithms
Unsupervised Cross-Lingual Representation Learning (P19-4)
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| Challenge: | a comprehensive survey of cutting-edge weakly-supervised and unsupervised cross-lingual word representations is presented . |
| Approach: | This tutorial provides a comprehensive survey of recent work on weakly-supervised and unsupervised cross-lingual word representations. |
| Outcome: | This tutorial provides a comprehensive survey of cutting-edge weakly-supervised and unsupervised word representations. |
Hybrid Inverted Index Is a Robust Accelerator for Dense Retrieval (2023.emnlp-main)
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| Challenge: | Inverted file structure is a common technique for accelerating dense retrieval, but its lossy nature degrades it. |
| Approach: | They propose a hybrid index where embedding clusters and salient terms work collaboratively to accelerate dense retrieval. |
| Outcome: | The proposed method achieves lossless retrieval quality with competitive efficiency across index settings. |
Beyond Borders: Investigating Cross-Jurisdiction Transfer in Legal Case Summarization (2024.naacl-long)
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| Challenge: | a study explores the cross-jurisdictional generalizability of legal case summarization models . fine-tuning on non-target datasets outperforms unsupervised methods, but success depends on similarity between source and target jurisdictions. |
| Approach: | They explore how to effectively summarize legal cases of a target jurisdiction where reference summaries are not available. |
| Outcome: | The proposed model can be generalized across jurisdictions and improve transfer performance. |
A Weak Supervision Approach for Predicting Difficulty of Technical Interview Questions (2022.coling-1)
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| Challenge: | Existing models require large volumes of candidate response data to train . Existing approaches require large amounts of candidate data to generate questions and generate models. |
| Approach: | They create a dataset of interview questions with difficulty scores for deep learning and use it to evaluate SOTA models trained using weak supervision. |
| Outcome: | The proposed model improves the difficulty and promise of weak supervision for interview questions and identifies the potential for weak supervision. |
Effects of sub-word segmentation on performance of transformer language models (2023.emnlp-main)
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| Challenge: | Language models are a fundamental task in natural language processing, but few studies focus on the effect of sub-word segmentation on the performance of models. |
| Approach: | They compare GPT and BERT models trained with statistical segmentation algorithm BPE to unsupervised morphological segmentation algorithms Morfessor and StateMorph. |
| Outcome: | The proposed model trains for several languages and compares them with two unsupervised morphological segmentation algorithms. |