Adversarial NLI: A New Benchmark for Natural Language Understanding (2020.acl-main)
Copied to clipboard
| Challenge: | a new large-scale NLI benchmark dataset is presented to test models on a variety of popular NLIs. |
| Approach: | They propose a large-scale NLI benchmark dataset that is iteratively compared with a human-and-model-in-the-loop procedure. |
| Outcome: | The proposed method can be applied in a never-ending learning scenario, becoming a moving target for NLU, rather than a static benchmark that will quickly saturate. |
Similar Papers
What Will it Take to Fix Benchmarking in Natural Language Understanding? (2021.naacl-main)
Copied to clipboard
| Challenge: | Evaluation for many natural language understanding (NLU) tasks is broken due to unreliable and biased systems scoring so high on standard benchmarks. |
| Approach: | They argue that current benchmarks fail at four criteria for evaluation . they argue that adversarial data collection does not address the causes of failures . |
| Outcome: | The proposed frameworks fail at four criteria, and adversarial data collection does not address the causes of these failures, the authors argue . restoring a healthy evaluation ecosystem will require significant progress in the design of benchmark datasets, reliability with which they are annotated, their size, and the ways they handle social bias. |
Deep Learning for Natural Language Inference (N19-5)
Copied to clipboard
| Challenge: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development, cutting- edge deep learning models, and highlights from recent research on using NLI to understand capabilities and limits of deep learning for language understanding and reasoning. |
| Approach: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development and cutting- edge deep learning models. |
| Outcome: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development, cutting- edge deep learning models, and highlights from recent research on using NLI to understand capabilities and limits of deep learning model for language understanding and reasoning. |
Tougher Text, Smarter Models: Raising the Bar for Adversarial Defence Benchmarks (2025.coling-main)
Copied to clipboard
| Challenge: | Recent advances in natural language processing have highlighted the vulnerability of deep learning models to adversarial attacks. |
| Approach: | They propose a benchmark for textual adversarial defence that evaluates state-of-the-art defence mechanisms across diverse datasets, models, and tasks. |
| Outcome: | The proposed benchmark incorporates a wide range of datasets and evaluates state-of-the-art defence mechanisms. |
Deep Adversarial Learning for NLP (N19-5)
Copied to clipboard
| Challenge: | Adversarial learning is a game-theoretic learning paradigm that has achieved huge successes in the field of Computer Vision recently. |
| Approach: | This tutorial introduces the foundations of deep adversarial learning and some practical problems and solutions in NLP. |
| Outcome: | This tutorial introduces the foundations of deep adversarial learning and some practical problems and solutions in NLP. |
Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training (2020.emnlp-main)
Copied to clipboard
| Challenge: | Neural models pick up on annotation artefacts and spurious correlations, resulting in learning sentences that suffer from the same biases. |
| Approach: | They propose to tackle this problem by using adversarial training to reduce the bias in sentence representations by using an ensemble of adversaries. |
| Outcome: | The proposed approach produces more robust models outperforming previous de-biasing efforts when generalised to 12 other NLI datasets. |
A guide to the dataset explosion in QA, NLI, and commonsense reasoning (2020.coling-tutorials)
Copied to clipboard
| Challenge: | a tutorial aims to provide an up-to-date guide to the recent datasets . the target audience is the NLP practitioners who are lost in dozens of the recent data sets. |
| Approach: | This tutorial provides an up-to-date guide to the recent datasets . it surveys old and new methodological issues with dataset construction . |
| Outcome: | This tutorial aims to provide an up-to-date guide to the recent datasets . it surveys the old and new methodological issues with dataset construction . |
Breaking NLI Systems with Sentences that Require Simple Lexical Inferences (P18-2)
Copied to clipboard
| Challenge: | a new test set shows the deficiency of state-of-the-art models in inferences that require lexical and world knowledge. |
| Approach: | They create a new NLI test set that shows the deficiency of state-of-the-art models in inferences that require lexical and world knowledge. |
| Outcome: | The new examples are simpler than the SNLI test set, but the state-of-the-art systems perform poorly on it. |
End-to-End Bias Mitigation by Modelling Biases in Corpora (2020.acl-main)
Copied to clipboard
| Challenge: | Recent studies have shown that strong natural language understanding models are prone to relying on unwanted dataset biases without learning the underlying task. |
| Approach: | They propose two learning strategies to train neural models that are more robust to dataset biases and transfer better to out-of-domain datasets. |
| Outcome: | The proposed methods improve robustness in all settings and transfer better to out-of-domain datasets. |
AD-NLP: A Benchmark for Anomaly Detection in Natural Language Processing (2023.emnlp-main)
Copied to clipboard
| Challenge: | Methods for Anomaly Detection in text have shown strong empirical results on ad-hoc anomaly setups that are usually made by downsampling some classes of a labeled dataset. |
| Approach: | They propose a unified benchmark for detecting various types of anomalies . they evaluate two strong shallow baselines and two current state-of-the-art neural approaches . |
| Outcome: | The proposed benchmarks provide insights into the knowledge the neural models are learning when performing the task. |
Robustness and Adversarial Examples in Natural Language Processing (2021.emnlp-tutorials)
Copied to clipboard
| Challenge: | This tutorial aims to raise awareness of practical concerns about NLP robustness . it aims at addressing the weaknesses of NLP systems when faced with adversarial inputs and data with a distribution shift . |
| Approach: | This tutorial aims to bring awareness of practical concerns about NLP robustness . it reviews recent studies on analyzing the weakness of NLP systems when facing adversarial inputs . |
| Outcome: | This tutorial aims to bring awareness of practical concerns about NLP robustness . it will examine the weaknesses of NLP systems when faced with adversarial inputs and data with a distribution shift . |