Papers with NLI tasks
Towards Robust and Semantically Organised Latent Representations for Unsupervised Text Style Transfer (2022.naacl-main)
Copied to clipboard
| Challenge: | Recent studies show that auto-encoders perform language generation, smooth sentence interpolation, and style transfer over unseen attributes using unlabelled datasets in a zero-shot manner. |
| Approach: | They propose a discrete token-based perturbation approach to map "similar" sentences close by in latent space. |
| Outcome: | The proposed model can generate and perform language generation, style transfer and sentence interpolation tasks on unlabelled datasets in a zero-shot manner. |
DocInfer: Document-level Natural Language Inference using Optimal Evidence Selection (2022.emnlp-main)
Copied to clipboard
| Challenge: | Documentlevel NLI is an important problem for many tasks including verification of factual correctness of documents. |
| Approach: | They propose a document-level natural language inference model that builds a hierarchical document graph enriched through inter-sentence relations and performs paragraph pruning using the novel SubGraph Pooling layer. |
| Outcome: | The proposed model performs on a legal judicial reasoning task with a dataset enriched with document graphs and a proposed evidence selection algorithm. |
Leveraging Moment Injection for Enhanced Semi-supervised Natural Language Inference with Large Language Models (2025.naacl-short)
Copied to clipboard
| Challenge: | Existing studies have used class-specific fine-tuned large language models to generate hypotheses and assign pseudo-labels but discarded many LLM-constructed samples to ensure the quality. |
| Approach: | They propose to leverage LLM-constructed samples by injecting the moments of labeled samples during training to properly adjust the level of noise. |
| Outcome: | The proposed method outperforms strong baselines on multiple NLI datasets in low-resource settings. |
”Diversity and Uncertainty in Moderation” are the Key to Data Selection for Multilingual Few-shot Transfer (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing approaches for few-shot transfer show significant gain over zero-shot transfers . language resource distribution is skewed across the world's languages . proposed methods use multiple measures such as data entropy and gradient embedding . |
| Approach: | They propose a loss embedding method for sequence labeling tasks that induces diversity and uncertainty sampling similar to gradient embeddment. |
| Outcome: | The proposed methods outperform baseline methods for POS tagging, NER, and NLI tasks for up to 20 languages. |
It is not True that Transformers are Inductive Learners: Probing NLI Models with External Negation (2024.eacl-long)
Copied to clipboard
| Challenge: | et al., 2017) show that NLI models learn to treat external negation as a distractor . e-learning models fail to inductively learn the role of negation for NLI tasks . |
| Approach: | They propose that models fine-tuned on NLI datasets learn to treat external negation as a distractor, effectively ignoring its presence in hypothesis sentences. |
| Outcome: | The proposed models learn to treat external negation as a distractor, the authors show . they also fail to inductively learn the law of the excluded middle for a single prefix . |
SETI: Systematicity Evaluation of Textual Inference (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing pre-trained language models (PLMs) have shown remarkable performance on this task, but little is known about their ability to address compositional generalization. |
| Approach: | They propose a benchmark to evaluate pre-trained language models' systematicity in the domain of textual inference. |
| Outcome: | The proposed benchmark evaluates pre-trained language models on six widely used PLMs. |
Knowledge-Enhanced Evidence Retrieval for Counterargument Generation (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models lack the reasoning abilities needed to find complex counterevidence. |
| Approach: | They propose a natural language inference model that finds counterevidence from diverse sources on the Web. |
| Outcome: | The proposed model outperforms baseline models for NLI tasks and finds complex counterevidence better. |
Can Large Language Models Capture Dissenting Human Voices? (2023.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) have shown impressive achievements in solving a broad range of tasks. |
| Approach: | They evaluate the performance and alignment of large language models with humans using Monte Carlo Estimation and Log Probability Estimationic methods to estimate the multinomial distribution. |
| Outcome: | The proposed models fail to capture human disagreement distribution and inference and human alignment performance plunge even further on data samples with high disagreement levels raising concerns about their natural language understanding ability and representativeness to a larger human population. |
MonoByte: A Pool of Monolingual Byte-level Language Models (2022.coling-1)
Copied to clipboard
| Challenge: | Existing studies have shown that multilingual models can achieve zero-shot cross-lingual performance on various NLP tasks, but due to the cost of pretraining, they often use public models with limited budgets. |
| Approach: | They propose to use tokenized models to test cross-lingual ability in multilingual and monolingual corpora. |
| Outcome: | The results show that models pretrained on multilingual and even monolingual corpora perform better than models pre-trained on SOTA models. |
Mining Knowledge for Natural Language Inference from Wikipedia Categories (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Accurate lexical entailment (LE) and natural language inference (NLI) tasks require expensive annotations. |
| Approach: | They propose to pretrain Wikipedia categories for lexical entailment and natural language inference by pretraining them on WikiNLI and transferring them to other knowledge bases. |
| Outcome: | The proposed model can improve strong baselines such as BERT and RoBERTa by pretraining on WikiNLI and transferring the models on downstream tasks. |
Leveraging Data Recasting to Enhance Tabular Reasoning (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to create tabular inference data are limited by human annotation and synthetic generation. |
| Approach: | They propose a framework for semi-automatically recasting tabular data to make use of both approaches. |
| Outcome: | The proposed framework can be used to build tabular NLI instances from five datasets. |
Learning to Infer from Unlabeled Data: A Semi-supervised Learning Approach for Robust Natural Language Inference (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Semi-supervised learning (SSL) is a popular technique for reducing the reliance on human annotations for NLI tasks. |
| Approach: | They propose a way to incorporate unlabeled data into semi-supervised learning (SSL) using a conditional language model, they propose to generate hypotheses for unlabed sentences . |
| Outcome: | The proposed framework significantly improves the performance of four NLI datasets in low-resource settings. |
Vector-Quantized Input-Contextualized Soft Prompts for Natural Language Understanding (2022.emnlp-main)
Copied to clipboard
| Challenge: | Prompt Tuning has been successful as a parameter-efficient method of conditioning large-scale pre-trained language models to perform downstream tasks. |
| Approach: | They propose to use a vector-quantized input-contextualized prompt as an extension to the soft prompt tuning framework to learn contextualization of soft prompt tokens. |
| Outcome: | The proposed prompt outperforms soft prompt tuning by an average margin of 1.19% on various language understanding tasks like SuperGLUE, QA, Relation classification, NER and NLI. |
Lost in Inference: Rediscovering the Role of Natural Language Inference for Large Language Models (2025.naacl-long)
Copied to clipboard
| Challenge: | In the recent past, a popular way of evaluating natural language understanding was to consider a model’s ability to perform natural language inference (NLI) tasks. |
| Approach: | They focus on five different NLI benchmarks across six models of different scales and examine how their accuracies develop during training. |
| Outcome: | The softmax distributions of models align with human label distributions in cases where statements are ambiguous or vague. |
Improve Dense Passage Retrieval with Entailment Tuning (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for dense retrieval have demonstrated remarkable performance in IR tasks. |
| Approach: | They propose a method to improve the embedding of dense retrievers by using existence claim as a bridge. |
| Outcome: | The proposed method can be plugged into current dense retrieval methods and the results are published in the journal Nature. |
Discriminatively-Tuned Generative Classifiers for Robust Natural Language Inference (2020.emnlp-main)
Copied to clipboard
| Challenge: | Recent work has shown advantages of generative classifiers in terms of data efficiency and robustness. |
| Approach: | They propose a generative classifier for natural language inference (NLI) they compare it to discriminative models and large-scale pretrained models like BERT . |
| Outcome: | The proposed classifier outperforms discriminative and pretrained baselines across several challenging NLI experimental settings, including small training sets, imbalanced label distributions, and label noise. |
Uncertain Natural Language Inference (2020.acl-main)
Copied to clipboard
| Challenge: | Existing categorical bin assignments for NLI are not effective for entailment inference. |
| Approach: | They propose a refinement of Natural Language Inference that targets direct prediction of subjective probability assessments. |
| Outcome: | The proposed model can be used in pre-training and correlates well with humans. |
New Datasets for Automatic Detection of Textual Entailment and of Contradictions between Sentences in French (2024.lrec-main)
Copied to clipboard
| Challenge: | DACCORD is a dataset for automatic detection of contradictions between sentences . it is the first dataset exclusively dedicated to this task . |
| Approach: | They introduce DACCORD, a dataset in French for automatic detection of contradictions between sentences. |
| Outcome: | The proposed datasets are more challenging than existing datasets for the mainstream task in French. |