VarMAE: Pre-training of Variational Masked Autoencoder for Domain-adaptive Language Understanding (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Pre-trained language models have been widely applied to standard benchmarks due to the limited resources available in a domain. |
| Approach: | They propose a Transformer-based language model called VarMAE for domain-adaptive language understanding that encodes the context of a token into a smooth latent distribution. |
| Outcome: | Experiments on science- and finance-domain NLU tasks show that the proposed model can be efficiently adapted to new domains with limited resources. |
Similar Papers
Neural Mask Generator: Learning to Generate Adaptive Word Maskings for Language Model Adaptation (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to train language models on diverse text corpora have brought up performance improvements on several natural language understanding (NLU) tasks. |
| Approach: | They propose a method to automatically generate domain- and task-adaptive maskings of a given text for self-supervised pre-training. |
| Outcome: | The proposed framework outperforms rule-based masking strategies on question answering and text classification datasets on which it outperformed rule-driven masking techniques. |
TADA: Efficient Task-Agnostic Domain Adaptation for Transformers (2023.findings-acl)
Copied to clipboard
| Challenge: | Pre-trained transformer-based language models are limited in their expressiveness and domain knowledge. |
| Approach: | They propose a task-agnostic domain adaptation method which is modular, parameter-efficient, and data-efficient. |
| Outcome: | The proposed method is efficient and modular, parameter-efficient, and data-efficient. |
VECO: Variable and Flexible Cross-lingual Pre-training for Language Understanding and Generation (2021.acl-long)
Copied to clipboard
| Challenge: | Existing work in multilingual pretraining relies on the shared vocabulary and bilingual contexts to encourage the correlation across languages. |
| Approach: | They propose to plug a cross-attention module into a Transformer encoder to explicitly build the interdependence between languages. |
| Outcome: | The proposed model outperforms existing models on XTREME and English-to-French translation datasets. |
NarrowBERT: Accelerating Masked Language Model Pretraining and Inference (2023.acl-short)
Copied to clipboard
| Challenge: | Large-scale language model pretraining is expensive as the models and pretraining corpora have become larger over time. |
| Approach: | They propose a modified transformer encoder that increases throughput for masked language model pretraining by more than 2x. |
| Outcome: | The proposed model increases throughput on IMDB and Amazon reviews classification and CoNLL NER tasks by 3.5x with minimal performance degradation. |
Fuse It More Deeply! A Variational Transformer with Layer-Wise Latent Variable Inference for Text Generation (2022.naacl-main)
Copied to clipboard
| Challenge: | Variational Auto-Encoders are often used for text generation tasks due to the sequential nature of the text. |
| Approach: | They propose a variational Transformer framework that learns a series of layer-wise latent variables with each inferred from those of lower layers and tightly coupled with the hidden states by low-rank tensor product. |
| Outcome: | The proposed framework can learn latent variables from lower layers and incorporate more information. |
Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing models for language understanding and understanding can be trained to provide contextualized representations of words based on text data. |
| Approach: | They propose a large-scale language VAE model Optimus that is pre-trained on large text corpus and fine-tuned for various language generation and understanding tasks. |
| Outcome: | The proposed model achieves new state-of-the-art on VAE language modeling benchmarks. |
Probabilistic Transformer: A Probabilistic Dependency Model for Contextual Word Representation (2023.findings-acl)
Copied to clipboard
| Challenge: | Syntactic structures were deemed essential in natural language processing . but since the deep learning revolution, NLP has been dominated by neural models that do not consider syntactical structures in their design. |
| Approach: | They propose a model that models latent representations of words in a sentence . they use a conditional random field to model latent and dependency arcs . |
| Outcome: | The proposed model performs competitively to transformers on small to medium sized datasets. |
TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to learn sentence embeddings require labeled data, but it is expensive. |
| Approach: | They propose an unsupervised method which learns sentence embeddings using unlabeled data . they propose a transformer-based sequence denoising auto-encoder which can be used for training . |
| Outcome: | The proposed method outperforms existing methods on four datasets from heterogeneous domains. |
Learning Variational Word Masks to Improve the Interpretability of Neural Text Classifiers (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for improving model interpretability require prior information or human annotations as additional inputs. |
| Approach: | They propose a variational word mask method to automatically learn task-specific important words and reduce irrelevant information on classification, which ultimately improves model interpretability. |
| Outcome: | The proposed method improves model prediction accuracy and interpretability on seven datasets. |
Selective Prefix Tuning for Pre-trained Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for fine-tuning pre-trained models are time-consuming and memory-inefficient. |
| Approach: | They propose a method that inserts learnable vectors into each Transformer layer . they propose SL to encourage diversity in prefix tokens . |
| Outcome: | Extensive experiments validate the effectiveness of Prefix Tuning in sentence and token classification tasks. |