| Challenge: | Several approaches to neural speed reading have been presented at major NLP and machine learning conferences in 2017–20. |
| Approach: | They propose to model "human speed reading" for more efficient NLP, including document classification and named entity recognition. |
| Outcome: | The proposed approach has 7% error reduction and 136x speed-up over the state-of-the-art in neural speed reading. |
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Rethinking Complex Neural Network Architectures for Document Classification (N19-1)
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| Challenge: | Neural network models for many NLP tasks have grown increasingly complex in recent years . authors of recent papers question the necessity of such architectures and find them quite effective . |
| Approach: | They propose to use regularization techniques borrowed from language modeling to improve model accuracy . they find that a simple biLSTM architecture with appropriate regularization yields competitive results . |
| Outcome: | a simple biLSTM model outperforms the state-of-the-art on four benchmark datasets . authors say that improvements are not real, but are attributed to mundane reasons . |
Improving Machine Reading Comprehension with General Reading Strategies (N19-1)
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| Challenge: | Recent studies have shown that reading strategies improve comprehension levels for readers lacking adequate prior knowledge. |
| Approach: | They propose three general strategies to improve machine reading comprehension (MRC) by fine-tuning a pre-trained model with strategies and a target task. |
| Outcome: | The proposed models improve non-extractive machine reading comprehension (MRC) on the largest general domain multiple-choice dataset RACE. |
Searching for Effective Neural Extractive Summarization: What Works and What’s Next (P19-1)
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| Challenge: | Recent years have seen success in the use of deep neural networks on text summarization, but there is no clear understanding of why they perform so well or how they might be improved. |
| Approach: | They propose to use different types of model architectures to improve extractive summarization systems. |
| Outcome: | The proposed framework achieves state-of-the-art on CNN/DailyMail by a large margin based on observations and analysis. |
Cut to the Chase: A Context Zoom-in Network for Reading Comprehension (D18-1)
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| Challenge: | Recent deep-learning based models suffer from reasoning over long documents and do not trivially generalize to cases where the answer is not present as a span. |
| Approach: | They propose a novel context zoom-in network (ConZNet) that can skip through irrelevant parts of a document and generate an answer using only the relevant regions of text. |
| Outcome: | The proposed architecture outperforms state-of-the-art results by 12.62% (ROUGE-L) relative improvement on the recently proposed and challenging RC dataset ‘NarrativeQA’. |
Even the Simplest Baseline Needs Careful Re-investigation: A Case Study on XML-CNN (2022.naacl-main)
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| Challenge: | XML-CNN has been a popular research topic in NLP due to its superior performance . however, the increasing complexity brings difficulties to ensure the true architectural progress . |
| Approach: | They propose to re-examine an influential multi-label text classification method . they propose suitable baselines for multi-level text classification tasks . |
| Outcome: | The proposed method performs better than the original model, the authors show . they show that the re-implementation reveals contradictory results to the original work . |
Adversarial NLI: A New Benchmark for Natural Language Understanding (2020.acl-main)
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| 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. |
Machine Reading, Fast and Slow: When Do Models “Understand” Language? (2022.coling-1)
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| Challenge: | Existing models of reading comprehension score highly on NLU benchmarks, but they are often 'read fast', i.e. rely on shallow patterns. |
| Approach: | They propose a definition for the reasoning steps expected from a system that would be 'reading slowly' they compare that behavior with five models of the BERT family of various sizes, observed through saliency scores and counterfactual explanations. |
| Outcome: | The proposed model is compared with five models of the BERT family of various sizes, and compared using saliency scores and counterfactual explanations. |
What’s Going On in Neural Constituency Parsers? An Analysis (N18-1)
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| Challenge: | a number of differences have emerged between classical and modern constituency parsing approaches . structural components like grammars and feature-rich lexicons are becoming less central . recurrent neural networks have gained traction as a powerful and general purpose tool for representation . |
| Approach: | They propose a model that implicitly learns to encode much of the same information as grammars and lexicons in the past. |
| Outcome: | The proposed model outperforms state-of-the-art models under similar conditions. |
An Evaluation of Progressive Neural Networksfor Transfer Learning in Natural Language Processing (2020.lrec-1)
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Abdul Moeed, Gerhard Hagerer, Sumit Dugar, Sarthak Gupta, Mainak Ghosh, Hannah Danner, Oliver Mitevski, Andreas Nawroth, Georg Groh
| Challenge: | Fine-tuning suffers from catastrophic forgetting, a problem exacerbated in natural language processing (NLP). |
| Approach: | They propose to use progressive neural networks to re-use previously learned knowledge when learning new tasks. |
| Outcome: | The proposed approach improves on common NLP tasks across a range of architectures, datasets, and tasks. |
Non-neural Models Matter: a Re-evaluation of Neural Referring Expression Generation Systems (2022.acl-long)
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| Challenge: | In recent years, neural models have outperformed rule-based and classic approaches in NLG. |
| Approach: | They evaluate two English datasets and evaluate their performance using automatic and human evaluations. |
| Outcome: | The proposed model outperforms rule-based and classic approaches on two English datasets and is compared with human-based models. |