| Challenge: | Neural networks (NNs) perform state-of-the-art (SOA) performance in many complex tasks. |
| Approach: | They investigate the importance that a model assigns to various aspects of data . they experiment with two strategies of masking to mitigate this dependence on lexicalized information . |
| Outcome: | The proposed model improves on the in-domain dataset by 10% compared to the fully lexicalized model. |
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| Challenge: | Delexicalization of datasets can improve natural language inference performance . a dataset with a delexicalized version of the FEVER dataset is used for natural language learning . |
| Approach: | They propose two techniques for delexicalization that modify annotated datasets to control the importance of lexical entities. |
| Outcome: | The proposed methods maintain performance in-domain and improve performance in some out-of-domain settings. |
Beyond Distributional Hypothesis: Let Language Models Learn Meaning-Text Correspondence (2022.findings-naacl)
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| Challenge: | Recent evidence shows that large-size pre-trained language models do not satisfy the logical negation property (LNP) However, their reliability is being challenged due to faulty behaviours and incomprehension on number-related representations. |
| Approach: | They propose a new intermediate training task to directly learn meaning text correspondence instead of relying on the distributional hypothesis. |
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Predicting generalization performance with correctness discriminators (2024.findings-emnlp)
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| Challenge: | Existing models estimate accuracy of models on unlabeled test data, but they hide their own uncertainty. |
| Approach: | They propose a model that establishes upper and lower bounds on the accuracy without requiring gold labels for the unseen data. |
| Outcome: | The proposed model establishes upper and lower bounds on accuracy without requiring gold labels for the unseen data. |
Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases (2021.acl-long)
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| Challenge: | Recent studies show that pre-trained masked language models can be factual knowledge bases. |
| Approach: | They conduct a rigorous study to explore the underlying predicting mechanisms of MLMs . they find that previous decent performance mainly owes to the biased prompts which overfit dataset artifacts a . |
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Separating Retention from Extraction in the Evaluation of End-to-end Relation Extraction (2021.emnlp-main)
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| Challenge: | State-of-the-art NLP models adopt shallow heuristics that limit their generalization capability. |
| Approach: | They propose to use heuristics that limit their generalization capability to model lexical overlap with the training set in Named-Entity Recognition and Event or Type heuristic in Relation Extraction to test their models. |
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Performance Impact Caused by Hidden Bias of Training Data for Recognizing Textual Entailment (L18-1)
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| Challenge: | a method to improve the quality of training data is needed . annotation errors of dialog act corpus mislead learning results of Bayesian network . |
| Approach: | They propose to introduce a null hypothesis for predictability of textual entailment labels and test it using a Naive Bayes model. |
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Exploring and Predicting Transferability across NLP Tasks (2020.emnlp-main)
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Tu Vu, Tong Wang, Tsendsuren Munkhdalai, Alessandro Sordoni, Adam Trischler, Andrew Mattarella-Micke, Subhransu Maji, Mohit Iyyer
| Challenge: | Recent advances in NLP demonstrate the effectiveness of training large-scale language models and transferring them to downstream tasks. |
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Assessing Word Importance Using Models Trained for Semantic Tasks (2023.findings-acl)
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| Challenge: | Many NLP tasks require to automatically identify the most significant words in a text. |
| Approach: | They propose to use attribution methods to explain the predictions of two NLP tasks to derive word significance from models trained to solve semantic tasks. |
| Outcome: | The proposed method is robust to the initial task and is able to identify important words in sentences without explicit word importance labeling in training. |
Learning to Infer from Unlabeled Data: A Semi-supervised Learning Approach for Robust Natural Language Inference (2022.findings-emnlp)
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| 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 . |
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Is Attention Interpretable? (P19-1)
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| Challenge: | Attention mechanisms have recently boosted performance on a range of NLP tasks. |
| Approach: | They propose to manipulate attention weights in text classification models and analyze the resulting differences in their predictions. |
| Outcome: | The proposed approach improves models' predictions by using gradient-based rankings of attention weights. |