A Primer on Contrastive Pretraining in Language Processing: Methods, Lessons Learned and Perspectives

• URL: http://arxiv.org/abs/2102.12982v1
• Date: Thu, 25 Feb 2021 16:35:07 GMT
• Title: A Primer on Contrastive Pretraining in Language Processing: Methods, Lessons Learned and Perspectives
• Authors: Nils Rethmeier and Isabelle Augenstein
• Abstract summary: We describe recent self-supervised and supervised contrastive NLP pretraining methods. We introduce key contrastive learning concepts with lessons learned from prior research and structure works by applications. We point to open challenges and future directions for contrastive NLP to encourage bringing contrastive NLP pretraining closer to recent successes in image representation pretraining.
• Score: 22.933794444266596
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern natural language processing (NLP) methods employ self-supervised pretraining objectives such as masked language modeling to boost the performance of various application tasks. These pretraining methods are frequently extended with recurrence, adversarial or linguistic property masking, and more recently with contrastive learning objectives. Contrastive self-supervised training objectives enabled recent successes in image representation pretraining by learning to contrast input-input pairs of augmented images as either similar or dissimilar. However, in NLP, automated creation of text input augmentations is still very challenging because a single token can invert the meaning of a sentence. For this reason, some contrastive NLP pretraining methods contrast over input-label pairs, rather than over input-input pairs, using methods from Metric Learning and Energy Based Models. In this survey, we summarize recent self-supervised and supervised contrastive NLP pretraining methods and describe where they are used to improve language modeling, few or zero-shot learning, pretraining data-efficiency and specific NLP end-tasks. We introduce key contrastive learning concepts with lessons learned from prior research and structure works by applications and cross-field relations. Finally, we point to open challenges and future directions for contrastive NLP to encourage bringing contrastive NLP pretraining closer to recent successes in image representation pretraining.

Related papers

• Pixel Sentence Representation Learning [67.4775296225521]
  In this work, we conceptualize the learning of sentence-level textual semantics as a visual representation learning process. We employ visually-grounded text perturbation methods like typos and word order shuffling, resonating with human cognitive patterns, and enabling perturbation to be perceived as continuous. Our approach is further bolstered by large-scale unsupervised topical alignment training and natural language inference supervision.
  arXiv  Detail & Related papers  (2024-02-13T02:46:45Z)
• Assessing Phrase Break of ESL Speech with Pre-trained Language Models and Large Language Models [7.782346535009883]
  This work introduces approaches to assessing phrase breaks in ESL learners' speech using pre-trained language models (PLMs) and large language models (LLMs)
  arXiv  Detail & Related papers  (2023-06-08T07:10:39Z)
• Pre-Trained Language-Meaning Models for Multilingual Parsing and Generation [14.309869321407522]
  We introduce multilingual pre-trained language-meaning models based on Discourse Representation Structures (DRSs) Since DRSs are language neutral, cross-lingual transfer learning is adopted to further improve the performance of non-English tasks. automatic evaluation results show that our approach achieves the best performance on both the multilingual DRS parsing and DRS-to-text generation tasks.
  arXiv  Detail & Related papers  (2023-05-31T19:00:33Z)
• Generative Negative Text Replay for Continual Vision-Language Pretraining [95.2784858069843]
  Vision-language pre-training has attracted increasing attention recently. Massive data are usually collected in a streaming fashion. We propose a multi-modal knowledge distillation between images and texts to align the instance-wise prediction between old and new models.
  arXiv  Detail & Related papers  (2022-10-31T13:42:21Z)
• Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling [101.74165219364264]
  Large-scale cross-lingual pre-trained language models (xPLMs) have shown effectiveness in cross-lingual sequence labeling tasks. Despite the great success, we draw an empirical observation that there is a training objective gap between pre-training and fine-tuning stages. In this paper, we first design a pre-training task tailored for xSL named Cross-lingual Language Informative Span Masking (CLISM) to eliminate the objective gap. Second, we present ContrAstive-Consistency Regularization (CACR), which utilizes contrastive learning to encourage the consistency between representations of input parallel
  arXiv  Detail & Related papers  (2022-04-11T15:55:20Z)
• AdaPrompt: Adaptive Model Training for Prompt-based NLP [77.12071707955889]
  We propose AdaPrompt, adaptively retrieving external data for continual pretraining of PLMs. Experimental results on five NLP benchmarks show that AdaPrompt can improve over standard PLMs in few-shot settings. In zero-shot settings, our method outperforms standard prompt-based methods by up to 26.35% relative error reduction.
  arXiv  Detail & Related papers  (2022-02-10T04:04:57Z)
• Adversarial Training with Contrastive Learning in NLP [0.0]
  We propose adversarial training with contrastive learning (ATCL) to adversarially train a language processing task. The core idea is to make linear perturbations in the embedding space of the input via fast gradient methods (FGM) and train the model to keep the original and perturbed representations close via contrastive learning. The results show not only an improvement in the quantitative (perplexity and BLEU) scores when compared to the baselines, but ATCL also achieves good qualitative results in the semantic level for both tasks.
  arXiv  Detail & Related papers  (2021-09-19T07:23:45Z)
• SLM: Learning a Discourse Language Representation with Sentence Unshuffling [53.42814722621715]
  We introduce Sentence-level Language Modeling, a new pre-training objective for learning a discourse language representation. We show that this feature of our model improves the performance of the original BERT by large margins.
  arXiv  Detail & Related papers  (2020-10-30T13:33:41Z)
• Pre-training Text Representations as Meta Learning [113.3361289756749]
  We introduce a learning algorithm which directly optimize model's ability to learn text representations for effective learning of downstream tasks. We show that there is an intrinsic connection between multi-task pre-training and model-agnostic meta-learning with a sequence of meta-train steps.
  arXiv  Detail & Related papers  (2020-04-12T09:05:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.