| Challenge: | Understanding contrastive opinions is a key component of argument generation. |
| Approach: | They create a corpus of Reddit comment pairs and train neural models to edit the original claim and produce a new claim with a different view. |
| Outcome: | The proposed model improves on a sequence-to-sequence baseline and compared to a human evaluation for fluency, coherence, and contrast. |
Similar Papers
KACE: Generating Knowledge Aware Contrastive Explanations for Natural Language Inference (2021.acl-long)
Copied to clipboard
| Challenge: | Existing approaches in NLP focus on “WHY A” rather than contrastive “WHA NOT B” Experimental results show that contrastive explanations are beneficial to fit the scenarios by clarifying the difference between the predicted answer and other possible wrong ones. |
| Approach: | They propose to generate contrastive explanations with counterfactual examples in NLI by identifying key phrases from input sentences and using them as key perturbations to generate counterfacts. |
| Outcome: | The proposed framework improves on SNLI and ETPA models by 91.9%. |
Prompting Contrastive Explanations for Commonsense Reasoning Tasks (2021.findings-acl)
Copied to clipboard
| Challenge: | Large pretrained language models (PLMs) can achieve near-human performance on commonsense reasoning tasks, but provide little human-interpretable evidence of the underlying reasoning they use. |
| Approach: | They propose to use large pretrained language models to generate evidence for commonsense reasoning NLP tasks . they use models to contrast alternative explanations based on key attribute(s) required to justify the correct answer . |
| Outcome: | The proposed model improves performance on two commonsense reasoning benchmarks compared to previous non-contrastive alternatives. |
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. |
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. |
Contrastive Multi-document Question Generation (2021.eacl-main)
Copied to clipboard
Woon Sang Cho, Yizhe Zhang, Sudha Rao, Asli Celikyilmaz, Chenyan Xiong, Jianfeng Gao, Mengdi Wang, Bill Dolan
| Challenge: | Multi-document question generation focuses on generating a question that covers the common aspect of multiple documents, but a naive model trained only using the targeted document set may generate too generic questions that cover a larger scope than delineated by the document set. |
| Approach: | They propose a contrastive learning strategy where given ‘positive’ and ‘negative’ sets of documents, generate a question that is closely related to the ‘positive' set but far away from the ‘negative' set. |
| Outcome: | The proposed model significantly outperforms several strong baselines, as measured by automatic metrics and human evaluation. |
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. |
Multi-Attribute Controlled Text Generation with Contrastive-Generator and External-Discriminator (2022.coling-1)
Copied to clipboard
| Challenge: | Existing studies on controlled text generation focus on single-attribute control, but in practical applications, they lack controllability. |
| Approach: | They propose a framework for multi-attribute controlled text generation that can effectively generate texts with more attributes. |
| Outcome: | The proposed framework achieves remarkable controllability while keeping the text fluent and diverse. |
Extracting the Essence and Discarding the Dross: Enhancing Code Generation with Contrastive Execution Feedback (2025.coling-main)
Copied to clipboard
| Challenge: | erroneous code generation methods amalgamate feedback and correct code as target sentences . a new approach to code generation with feedback is needed to improve model performance . |
| Approach: | They propose a learning-based code generation model with execution feedback that integrates feedback and correct code as target sentences. |
| Outcome: | a new model with execution feedback shows improvements in generating accurate code and understanding error correction. |
Explaining NLP Models via Minimal Contrastive Editing (MiCE) (2021.findings-acl)
Copied to clipboard
| Challenge: | Cognitive science and philosophy research has shown that human explanations are contrastive . a contrast case plays a key role in modulating what explanations can be given . |
| Approach: | They propose a method for producing contrastive explanations of model predictions . they edit models' outputs to change model outputs, and then edit them to the contrast case . |
| Outcome: | a new method produces contrastive explanations of model predictions in the form of edits . the edits are minimal and fluent, consistent with human contrastive edits. |
Explaining with Contrastive Phrasal Highlighting: A Case Study in Assisting Humans to Detect Translation Differences (2023.emnlp-main)
Copied to clipboard
| Challenge: | a common strategy to explain NLP predictions is to highlight salient tokens in their inputs. |
| Approach: | They propose a technique to generate contrastive phrasal highlights that explain the predictions of a semantic divergence model via phrase alignment guided erasure. |
| Outcome: | The proposed techniques match human rationales of cross-lingual semantic differences better than popular post-hoc saliency techniques and help people detect fine-grained meaning differences in human translations and critical machine translation errors. |