ProphetNet: Predicting Future N-gram for Sequence-to-SequencePre-training (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing sequence-to-sequence models are optimized for future n-gram prediction and n stream self-attention mechanism. |
| Approach: | They propose a self-supervised objective called future n-gram prediction and the proposed n stream self-attention mechanism to optimize the model for sequence-to-sequence learning. |
| Outcome: | The proposed model achieves state-of-the-art on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks compared to the models using the same scale pre-training corpus. |
Similar Papers
ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation (2021.acl-demo)
Copied to clipboard
Weizhen Qi, Yeyun Gong, Yu Yan, Can Xu, Bolun Yao, Bartuer Zhou, Biao Cheng, Daxin Jiang, Jiusheng Chen, Ruofei Zhang, Houqiang Li, Nan Duan
| Challenge: | Existing models for pre-training are not convenient for users to find and set them up. |
| Approach: | They propose to extend ProphetNet into other domains and languages by pre-training models . they pre-train a cross-lingual generation model ProphetNet-Multi and a Chinese generation model . |
| Outcome: | The proposed models achieve new state-of-the-art on 10 benchmarks. |
P3LM: Probabilistically Permuted Prophet Language Modeling for Generative Pre-Training (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing autoregressive left-to-right (L2R) models are limited to unidirectional information and constrained on strong local dependencies. |
| Approach: | They propose a probabilistically permuted prophet language model which strengthens the modeling of bidirectional information and long token dependencies for sequence generation. |
| Outcome: | Experiments on GLGE dataset show that P3LM improves on natural language generation tasks. |
Leveraging WordNet Paths for Neural Hypernym Prediction (2020.coling-main)
Copied to clipboard
| Challenge: | Existing work on lexical relations based on distributed representations has differed widely. |
| Approach: | They propose a model that generates taxonomy paths for hypernym prediction using WordNet sequences. |
| Outcome: | The hypo2path model outperforms the best model by 4.11 points in hit-at-one (H@1) The proposed model outpersforms previous models by a factor of 0.9. |
FutureTOD: Teaching Future Knowledge to Pre-trained Language Model for Task-Oriented Dialogue (2023.acl-long)
Copied to clipboard
| Challenge: | Existing pre-trained language models rely on a contrastive framework and are difficult to use in practice. |
| Approach: | They propose a dialogue pre-training model which distills future knowledge to the representation of the previous dialogue context using a self-training framework. |
| Outcome: | The proposed model can be applied to various downstream dialogue tasks. |
Efficient Training of Language Models with Compact and Consistent Next Token Distributions (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to train language models have focused on maximizing the likelihood of the next token . however, the construction and querying of such n-grams can be costly and impede training speed. |
| Approach: | They propose a method to train language models faster by pre-aggregating corpus with collapsed n-gram distribution. |
| Outcome: | The proposed model improves model quality and convergence rate while reducing variance across mini-batches compared to the standard next-token loss method. |
Attending to Future Tokens for Bidirectional Sequence Generation (D19-1)
Copied to clipboard
| Challenge: | Neural sequence generation is typically performed token-by-token and left-to-right. |
| Approach: | They propose to use placeholder tokens to make the sequence generation process bidirectional by taking past and future tokens into consideration when generating the actual output token. |
| Outcome: | The proposed approach outperforms baselines on two conversational tasks by a large margin. |
RecGPT: Generative Pre-training for Text-based Recommendation (2024.acl-short)
Copied to clipboard
| Challenge: | Existing models for text-based recommendation lack data sparsity and flexibility to capture fluctuations in user preferences over time. |
| Approach: | They present the first domain-adapted and fully-trained large language model for text-based recommendation. |
| Outcome: | The proposed model outperforms baseline models on rating prediction and sequential recommendation tasks. |
Target-Aware Language Modeling via Granular Data Sampling (2024.emnlp-main)
Copied to clipboard
Ernie Chang, Pin-Jie Lin, Yang Li, Changsheng Zhao, Daeil Kim, Rastislav Rabatin, Zechun Liu, Yangyang Shi, Vikas Chandra
| Challenge: | Language model pretraining is the cornerstone of universal language models (LMs), creating generalpurpose representations to excel across a variety of downstream tasks. |
| Approach: | They propose to use multi-granular tokens to sample large-scale language models for domain-specific use cases. |
| Outcome: | The proposed model outperforms random sampled samples on eight benchmarks with 1% of the data and performs on par with the full RefinedWeb data. |
Planning with Learned Entity Prompts for Abstractive Summarization (2021.tacl-1)
Copied to clipboard
| Challenge: | a simple but flexible mechanism is used to ground the generation of abstractive summaries. |
| Approach: | They propose a mechanism to learn an intermediate plan to ground the generation of abstractive summaries. |
| Outcome: | The proposed model outperforms state-of-the-art methods for faithfulness on CNN and BillSum. |
Leveraging Pre-trained Checkpoints for Sequence Generation Tasks (2020.tacl-1)
Copied to clipboard
| Challenge: | Unsupervised pre-training of large neural models has revolutionized Natural Language Processing. |
| Approach: | They propose to use pre-trained checkpoints for Sequence Generation to initialize a Transformer-based sequence-to-sequence model that is compatible with these checkpoint. |
| Outcome: | The proposed model is compatible with pre-trained BERT, GPT-2, and RoBERTa checkpoints and achieves state-of-the-art results on Machine Translation, Text Summarization, Sentence Splitting, and Sentance Fusion. |