Papers with n-gram language model
A Frustratingly Simple Decoding Method for Neural Text Generation (2024.lrec-main)
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| Challenge: | Neural text generation is notorious for repetitive loops and tedious outputs. |
| Approach: | They propose a method that penalizes future generation of repetitive content . they construct an anti-LM based on previously generated text . |
| Outcome: | The proposed method outperforms established baselines in terms of generation quality, decoding speed, and universality. |
”Diversity and Uncertainty in Moderation” are the Key to Data Selection for Multilingual Few-shot Transfer (2022.findings-naacl)
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| Challenge: | Existing approaches for few-shot transfer show significant gain over zero-shot transfers . language resource distribution is skewed across the world's languages . proposed methods use multiple measures such as data entropy and gradient embedding . |
| Approach: | They propose a loss embedding method for sequence labeling tasks that induces diversity and uncertainty sampling similar to gradient embeddment. |
| Outcome: | The proposed methods outperform baseline methods for POS tagging, NER, and NLI tasks for up to 20 languages. |
Language Model-Driven Data Pruning Enables Efficient Active Learning (2026.findings-eacl)
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| Challenge: | Existing data pruning methods for active learning are expensive and time-consuming. |
| Approach: | They propose a plug-and-play data pruning strategy that leverages language models to prune the unlabeled pool. |
| Outcome: | The proposed pruning strategy outperforms existing pruning methods on translation, sentiment analysis, topic classification, and summarization tasks on diverse datasets. |
Unsupervised Statistical Machine Translation (D18-1)
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| 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. |