| Challenge: | Text classification is one of the most widely studied tasks in natural language processing. |
| Approach: | They propose to use large multilayer neural network models to compose meaning of sentences . they propose to disincentivize focusing on key lexicons to improve classification accuracy . |
| Outcome: | The proposed models learn to compose the meaning of the sentences or focus on key lexicons for classifying the document. |
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| Challenge: | Current training data for text classification is limited, resulting in limited generalization capacity. |
| Approach: | They propose a feed-forward network that can generalize from unlabeled parsed corpora to produce task-specific semantic vectors. |
| Outcome: | The proposed approach is especially effective in low-data scenarios compared to state-of-the-art methods. |
Connecting the Dots: What Graph-Based Text Representations Work Best for Text Classification using Graph Neural Networks? (2023.findings-emnlp)
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| Challenge: | Graph Neural Networks have been used for text classification, but only in domains with limited data characteristics. |
| Approach: | They compare graph representation methods for text classification using different architectures and setups. |
| Outcome: | The proposed graph representation methods outperform other models in document comprehension tasks. |
Compositional Generalization by Factorizing Alignment and Translation (2020.acl-srw)
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| Challenge: | a crucial property underlying the expressive power of human language is its systematicity. |
| Approach: | They propose to make an analogous separation between alignment and translation in neural machine translation to capture compositional structure. |
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Text Segmentation as a Supervised Learning Task (N18-2)
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| Challenge: | Existing datasets for text segmentation are small in size and do not represent the natural distribution of text in documents. |
| Approach: | They propose a large dataset for text segmentation that is automatically extracted and labeled from Wikipedia and develop a model based on this dataset. |
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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. |
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On the (In)Effectiveness of Images for Text Classification (2021.eacl-main)
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| Challenge: | Existing studies have focused on text classification, but have shown that images do not improve NLP tasks. |
| Approach: | They focus on text classification, where images complement the text and the Wikipedia page can be in one of a number of different languages. |
| Outcome: | The proposed model trains without external pre-training, but when combined with BERT models pre-trained on large-scale external data, images contribute nothing. |
Linear Classifier: An Often-Forgotten Baseline for Text Classification (2023.acl-short)
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| Challenge: | Large-scale pre-trained language models such as BERT are popular solutions for text classification. |
| Approach: | They argue that large-scale pre-trained language models such as BERT are popular solutions for text classification . authors argue that running a simple baseline like linear classifiers on bag-of-words features is important for text classification . |
| Outcome: | The proposed approach may only sometimes get satisfactory results for some problems. |
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 . |
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Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks (2020.acl-main)
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| Challenge: | Existing graph-based methods for text classification cannot capture contextual word relationships within each document nor can they produce inductive learning of new words. |
| Approach: | They propose to use Graph Neural Networks to learn the local word representations and then aggregate the word nodes as the document embeddings. |
| Outcome: | The proposed method outperforms state-of-the-art methods on four benchmark datasets. |
Meta-Learning to Compositionally Generalize (2021.acl-long)
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| Challenge: | Existing studies show that neural networks struggle with compositional generalization . prior work asserts that there are fundamental differences between cognitive and connectionist architectures that make compositional globalization unlikely. |
| Approach: | They propose a meta-learning augmented version of supervised learning that optimizes for out-of-distribution generalization. |
| Outcome: | The proposed model improves generalization performance on COGS and SCAN datasets. |