A Self-supervised Joint Training Framework for Document Reranking (2022.findings-naacl)
Copied to clipboard
| Challenge: | Pretrained language models have been successfully applied to a wide range of tasks . however, the pretraining tasks were based on the context of documents . |
| Approach: | They propose a self-supervised joint training framework with a method called Masked Query Prediction to establish semantic relations between given queries and positive documents. |
| Outcome: | The proposed framework outperforms existing models on document reranking tasks without further pre-training . it uses a self-supervised method to establish semantic relations between given queries and positive documents. |
Similar Papers
Frustratingly Simple Pretraining Alternatives to Masked Language Modeling (2021.emnlp-main)
Copied to clipboard
| Challenge: | Masked language modeling (MLM) is widely used in natural language processing for self-supervised learning of text representations. |
| Approach: | They propose to use token-level classification tasks as main pretraining objectives instead of Masked language modeling (MLM) . Empirical results show that pretraining a model with 41% of the BERT-BASE’s parameters, BERT MEDIUM results in only a 1% drop in GLUE scores with their best objective. |
| Outcome: | Empirical results show that the proposed methods achieve comparable or better performance to MLM using a BERT-BASE architecture. |
Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment (2021.acl-long)
Copied to clipboard
| Challenge: | Experimental results show that denoising word alignment improves cross-lingual transferability . most applications and resources are still English-centric, making non-English users hard to access. |
| Approach: | They propose to denoise word alignment as a cross-lingual pre-training task . they first self-label word alignments for parallel sentences and then mask tokens . |
| Outcome: | The proposed model improves cross-lingual transferability on token-level tasks, especially on question answering, and structured prediction. |
LinkBERT: Pretraining Language Models with Document Links (2022.acl-long)
Copied to clipboard
| Challenge: | Existing language model pretraining methods do not capture dependencies or knowledge that span across documents. |
| Approach: | They propose a language model pretraining method that leverages links between documents . they use masked language modeling and document relation prediction to model LMs . |
| Outcome: | The proposed method outperforms existing methods on downstream tasks across two domains. |
Self-Evolution Learning for Discriminative Language Model Pretraining (2023.findings-acl)
Copied to clipboard
| Challenge: | Random masking does not consider the importance of the different words in the sentence meaning, e.g., entity-level masking requires expensive prior knowledge and generally does not use existing model weights. |
| Approach: | They propose a token masking and learning method that uses a random masking strategy to learn the under-explored tokens. |
| Outcome: | The proposed method improves linguistic knowledge learning and generalization on 10 tasks. |
Patton: Language Model Pretraining on Text-Rich Networks (2023.acl-long)
Copied to clipboard
| Challenge: | Existing models for text-rich networks do not take inter-document structure into account. |
| Approach: | They propose a pretraining framework for a text-rich network using a masked language model and a masking node prediction framework. |
| Outcome: | The proposed model outperforms baselines on four tasks in academic and e-commerce domains. |
Improving Pretraining Techniques for Code-Switched NLP (2023.acl-long)
Copied to clipboard
| Challenge: | Multilingual pretraining models for code-switched inputs are a key component of NLP applications. |
| Approach: | They propose to use masked language modeling techniques to mask code-switched text that are cognizant of language boundaries prior to masking. |
| Outcome: | The proposed techniques improve performance on two downstream tasks, Question Answering (QA) and Sentiment Analysis (SA), compared to standard pretraining techniques. |
Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach (2021.naacl-main)
Copied to clipboard
| Challenge: | Fine-tuned pre-trained language models (LMs) have enormous success in many natural language processing tasks, but they still require excessive labeled data in the fine-tuning stage. |
| Approach: | They propose a framework to enable fine-tuning pre-trained language models with weak supervision without any labeled data. |
| Outcome: | The proposed framework outperforms the strongest baseline and achieves competitive performance with fully-supervised fine-tuning methods. |
Masked Measurement Prediction: Learning to Jointly Predict Quantities and Units from Textual Context (2022.findings-naacl)
Copied to clipboard
| Challenge: | Current benchmarks do not evaluate numeracy of pretraining language models on measurements. |
| Approach: | They propose a new task where a model learns to reconstruct a number with its associated unit given masked text. |
| Outcome: | The proposed model significantly underperforms pre-trained model with baselines and ablations. |
DOCmT5: Document-Level Pretraining of Multilingual Language Models (2022.findings-naacl)
Copied to clipboard
| Challenge: | DOCmT5 is a multilingual sequence-to-sequence language model pretraining with large-scale parallel documents. |
| Approach: | They propose a multilingual sequence-to-sequence language model pretrained with large-scale parallel documents. |
| Outcome: | The proposed model improves on baselines on document-level generation tasks. |
A Robust Self-Learning Framework for Cross-Lingual Text Classification (D19-1)
Copied to clipboard
| Challenge: | Recent advances in pretrained contextual representation models have made significant progress on a number of different English NLP tasks. |
| Approach: | They propose a robust framework to include unlabeled non-English samples in the fine-tuning process of pretrained multilingual representation models. |
| Outcome: | The proposed framework includes unlabeled non-English samples in the fine-tuning process of pretrained multilingual representation models. |