Learning with Contrastive Examples for Data-to-Text Generation (2020.coling-main)
Copied to clipboard
Yui Uehara, Tatsuya Ishigaki, Kasumi Aoki, Hiroshi Noji, Keiichi Goshima, Ichiro Kobayashi, Hiroya Takamura, Yusuke Miyao
| Challenge: | Existing models for data-to-text generation generate fluent but sometimes incorrect sentences . Existing studies show that using contrastive examples improves the ability of generating sentences with better lexical choice without degrading the fluency. |
| Approach: | They propose to use models trained on incorrect sentences and learning methods that exploit contrastive examples to reduce such errors. |
| Outcome: | The proposed models generate fluent sentences but often have problematic ones in terms of correctness. |
Similar Papers
Contrastive Data and Learning for Natural Language Processing (2022.naacl-tutorials)
Copied to clipboard
| Challenge: | Current NLP models heavily rely on effective representation learning algorithms. |
| Approach: | This tutorial introduces contrastive learning and provides an introduction to the techniques. |
| Outcome: | This tutorial provides an introduction to the fundamentals of contrastive learning approaches and the theory behind them. |
Constructing contrastive samples via summarization for text classification with limited annotations (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Various contrastive learning methods have been developed and lead to state-of-the-art performance in many computer vision tasks. |
| Approach: | They propose a method to construct efficient contrastive samples using text summarization to gain better representations of text classification tasks with limited annotations. |
| Outcome: | The proposed framework gains better representations on text classification tasks with limited annotations and is compared with existing methods on real-world text classification datasets. |
Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval (2023.acl-long)
Copied to clipboard
| Challenge: | Contrastive learning is the dominant paradigm for learning text representations from parallel text, but finding negative examples can be expensive in terms of compute or manual effort. |
| Approach: | They propose a generative model for learning multilingual text embeddings which encourages source separation in multilingual contexts by an approximation. |
| Outcome: | The proposed model outperforms both a strong contrastive and generative baseline on a suite of tasks including semantic similarity, bitext mining, and cross-lingual question retrieval. |
Fine-grained Contrastive Learning for Definition Generation (2022.aacl-main)
Copied to clipboard
| Challenge: | Recent pre-trained transformer-based definition generation models lack effective representation learning to contain full semantic components of the given word, leading to under-specific definitions. |
| Approach: | They propose a novel contrastive learning method that encourages the model to capture more detailed semantic representations from the definition sequence encoding. |
| Outcome: | The proposed method could generate more specific definitions compared with state-of-the-art models. |
Batch-Softmax Contrastive Loss for Pairwise Sentence Scoring Tasks (2022.naacl-main)
Copied to clipboard
| Challenge: | Recent advances in machine learning have led to the use of contrastive loss for representation learning. |
| Approach: | They propose to use batch-softmax contrastive loss to train pairwise sentence embeddings . they propose to take a batch-softermax contrastitive loss and train it with different loss functions . |
| Outcome: | The proposed model improves on a number of datasets and pairwise sentence scoring tasks. |
The CRINGE Loss: Learning what language not to model (2023.acl-long)
Copied to clipboard
| Challenge: | Standard language model training uses gold human documents or human-human interaction data and treats all training data as positive examples. |
| Approach: | They propose a procedure to train with negative examples using the "CRINGE" loss technique and use it to train models with such data. |
| Outcome: | The proposed procedure outperforms multiple strong baselines and is simple to train and implement. |
SumCSE: Summary as a transformation for Contrastive Learning (2024.findings-naacl)
Copied to clipboard
| Challenge: | Sentence embedding models are typically trained using contrastive learning (CL) using human annotations directly or by repurposing other annotated datasets. |
| Approach: | They propose to use generative language models to generate CL data using annotated data. |
| Outcome: | The proposed method outperforms the previous best unsupervised method by 1.8 points and SimCSE, a strong supervised baseline by 0.3 points on the semantic text similarity (STS) benchmark. |
Differentiable Data Augmentation for Contrastive Sentence Representation Learning (2022.emnlp-main)
Copied to clipboard
| Challenge: | a contrastive learning framework is used to fine-tune pre-trained language models with unlabeled sentences or labeled sentences. |
| Approach: | They propose a method that makes hard positives from unlabeled sentences . they use a prefix attached to a model to allow for differentiable data augmentation . |
| Outcome: | The proposed method yields significant improvements over existing methods under semi-supervised and supervised settings. |
Adversarial Text Generation via Sequence Contrast Discrimination (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to generate human-like texts are auto-regressive, but they suffer from exposure bias due to the dependence on the previous sampled output during the inferring phase. |
| Approach: | They propose a sequence contrast loss driven text generation framework which learns the difference between real texts and generated texts and uses that difference. |
| Outcome: | The proposed framework improves training stability and quality of generated texts and avoids the time-consuming sampling process. |
Click: Controllable Text Generation with Sequence Likelihood Contrastive Learning (2023.findings-acl)
Copied to clipboard
| Challenge: | Current language models have shown impressive capability of generating fluent and grammatical text, but they often produce behaviors misaligned with human expectations. |
| Approach: | They propose a new language model called Leo for controllable text generation which employs a contrastive loss on sequence likelihood which fundamentally decreases the generation probability of negative samples. |
| Outcome: | The proposed model outperforms baselines on language detoxification, sentiment steering, and repetition reduction tasks. |