Papers with textual entailment models
Two-Step Classification using Recasted Data for Low Resource Settings (2020.aacl-main)
Copied to clipboard
Shagun Uppal, Vivek Gupta, Avinash Swaminathan, Haimin Zhang, Debanjan Mahata, Rakesh Gosangi, Rajiv Ratn Shah, Amanda Stent
| Challenge: | Existing studies on NLP models focus on high resource languages like English, but there are only two datasets for Hindi. |
| Approach: | They propose a novel two-step classification method which uses textual-entailment predictions for classification task. |
| Outcome: | The proposed method improves classification performance by using a joint-objective for classification and textual entailment. |
Logic Against Bias: Textual Entailment Mitigates Stereotypical Sentence Reasoning (2023.eacl-main)
Copied to clipboard
| Challenge: | Recent studies show that textual entailment learning reduces social biases in pretrained sentence encoders. |
| Approach: | They compare pretrained sentence encoders with textual entailment models that learn language logic for downstream language understanding tasks. |
| Outcome: | The proposed models outperform models with lower bias without debiasing processes on stereotype, profession, and emotion bias tests. |
Generating Natural Language Adversarial Examples (D18-1)
Copied to clipboard
| Challenge: | Recent research has shown that deep neural networks are vulnerable to adversarial examples, perturbations to correctly classified examples which can cause the model to misclassify. |
| Approach: | They propose to generate adversarial examples that fool well-trained sentiment analysis and textual entailment models by using a black-box population-based optimization algorithm. |
| Outcome: | The proposed model is able to fool well-trained sentiment analysis and textual entailment models with success rates of 97% and 70%, respectively. |
Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback (2023.acl-long)
Copied to clipboard
Paul Roit, Johan Ferret, Lior Shani, Roee Aharoni, Geoffrey Cideron, Robert Dadashi, Matthieu Geist, Sertan Girgin, Leonard Hussenot, Orgad Keller, Nikola Momchev, Sabela Ramos Garea, Piotr Stanczyk, Nino Vieillard, Olivier Bachem, Gal Elidan, Avinatan Hassidim, Olivier Pietquin, Idan Szpektor
| Challenge: | Recent advances in abstractive summarization systems produce factually inconsistent text . this is emphasized in tasks like summarizing, which often produce inconsistent text with no input article . |
| Approach: | They use reinforcement learning to optimize for factual consistency and explore trade-offs . they use textual-entailment rewards to optimize the accuracy of the generated summaries . |
| Outcome: | The proposed method improves faithfulness, salience and conciseness of the generated summaries. |
Don’t Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases (D19-1)
Copied to clipboard
| Challenge: | Recent advances in neural models exploit dataset-specific patterns that do not generalize well to out-of-domain or adversarial settings. |
| Approach: | They propose to train a model to be more robust to domain shift if it has prior knowledge of dataset biases. |
| Outcome: | The proposed model can be more robust to domain shift if it has prior knowledge of dataset biases. |
Bridging Knowledge Gaps in Neural Entailment via Symbolic Models (D18-1)
Copied to clipboard
| Challenge: | Textual entailment models focus on lexical gaps but rarely on knowledge gaps. |
| Approach: | They propose a fact-level decomposition of the hypothesis and a knowledge lookup module to fill knowledge gaps in Science Entailment task. |
| Outcome: | The proposed model outperforms the base model on the SciTail dataset by 3% and 5% on the textual premise and the structured knowledge base. |