GBT: Generative Boosting Training Approach for Paraphrase Identification (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Paraphrase Identification (PI) is a fundamental natural language understanding task with non-trivial challenges. |
| Approach: | They propose a Generative Boosting Training approach for Paraphrase Identification (PI) they use a seq2seq model to perform DA on misclassified instances periodically . |
| Outcome: | The proposed method outperforms state-of-the-art PI models on English and Chinese PI tasks with good efficiency and effectiveness. |
Similar Papers
ParaTag: A Dataset of Paraphrase Tagging for Fine-Grained Labels, NLG Evaluation, and Data Augmentation (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing datasets only annotate a binary label for each sentence pair. Existing models only annnotate binary labels for each phrase pair. |
| Approach: | They propose a novel binary paraphrase classification task that annotates the degree of paraphrase between sentences and a new annotation schema that labels the minimum spans of tokens in a sentence that don't have the corresponding paraphrases in the other sentence. |
| Outcome: | The proposed dataset can be used to train an automatic scorer for language generation evaluation. |
Knowledge-Guided Paraphrase Identification (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for paraphrase identification (PI) are limited due to lack of professional knowledge. |
| Approach: | They propose to leverage Wikipedia knowledge to accurately identify paraphrases by mining outline knowledge of given sentences from Wikipedia. |
| Outcome: | The proposed framework outperforms state-of-the-art models on two public datasets: PARADE and clinicalSTS2019. |
Controllable Paraphrase Generation for Semantic and Lexical Similarities (2024.lrec-main)
Copied to clipboard
| Challenge: | Lexically diverse paraphrases are crucial in data augmentation because they enhance the linguistic diversity of the corpus. |
| Approach: | They propose a controllable model for semantic and lexical similarities by attaching tags to the head of the input sentence. |
| Outcome: | The proposed model can paraphrase an input sentence according to the tags specified. |
Generative Data Augmentation for Commonsense Reasoning (2020.findings-emnlp)
Copied to clipboard
Yiben Yang, Chaitanya Malaviya, Jared Fernandez, Swabha Swayamdipta, Ronan Le Bras, Ji-Ping Wang, Chandra Bhagavatula, Yejin Choi, Doug Downey
| Challenge: | Recent advances in commonsense reasoning depend on large-scale human-authored training data. |
| Approach: | They propose a generative data augmentation technique that augments human-authored training data by using pretrained language models. |
| Outcome: | The proposed technique outperforms existing methods on commonsense reasoning benchmarks and enhances out-of-distribution generalization. |
Paraphrase Types for Generation and Detection (2023.emnlp-main)
Copied to clipboard
| Challenge: | Current approaches to paraphrase generation and detection ignore the intricate linguistic properties of language. |
| Approach: | They propose two tasks to consider specific linguistic perturbations at particular text positions. |
| Outcome: | The proposed tasks address the shortcoming of ignoring the linguistic properties of language. |
GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies report that prompt-based direct classification eliminates the need for fine-tuning but lacks data and inference scalability. |
| Approach: | They propose a data augmentation technique that leverages large-scale language models to generate real text samples from a mixture of real samples. |
| Outcome: | The proposed method outperforms existing methods on diverse classification tasks. |
ReFT: Reasoning with Reinforced Fine-Tuning (2024.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to improve the generalization of large language models are using Supervised Fine-Tuning (SFT) this approach does not show sufficient generalization ability because it only relies on the given CoT data. |
| Approach: | They propose to use Chain-of-Thought annotations to train Large Language Models using supervised fine-tuning to improve generalization. |
| Outcome: | The proposed approach outperforms SFT on GSM8K, MathQA, and SVAMP datasets and shows a superior generalization ability. |
Pointwise Paraphrase Appraisal is Potentially Problematic (2020.acl-srw)
Copied to clipboard
| Challenge: | prevailing methods for paraphrase identification models are binary classification problems . current methods do not provide consistent and robust performance on unseen samples and real world problems. |
| Approach: | They propose to use binary classification to evaluate paraphrase identification models . they propose to improve methods for fine-tuning BERT models by pairing two sentences as one sequence . |
| Outcome: | The proposed methods may fail on simple tasks like identifying pairs with two identical sentences. |
Generative Pretraining for Paraphrase Evaluation (2022.acl-long)
Copied to clipboard
| Challenge: | ParaBLEU is a paraphrase representation learning model and evaluation metric for text generation. |
| Approach: | They propose a paraphrase representation learning model and evaluation metric for text generation that uses generative conditioning as a pretraining objective. |
| Outcome: | The proposed model outperforms existing models on the 2017 WMT Metrics Shared Task using only 50% of the available training data and surpasses BLEU, ROUGE and METEOR with only 40 examples. |
How Large Language Models are Transforming Machine-Paraphrase Plagiarism (2022.emnlp-main)
Copied to clipboard
| Challenge: | Autoregressive paraphrasing tools can be used to generate convincing plagiarized texts with minimal effort. |
| Approach: | They evaluate the detection performance of large autoregressive models for machine-paraphrase generation on scientific articles from arXiv, student theses, and Wikipedia. |
| Outcome: | The proposed models generate paraphrases indistinguishable from original work and human experts rate the quality of generated examples as high as originals. |