| Challenge: | Convolutional neural networks (CNNs) are a popular building block for natural language processing . despite their success, most existing CNN models share the same learned set of filters for all input sentences. |
| Approach: | They propose to use a meta network to learn context-sensitive convolutional filters for text processing by using a bidirectional filter generation mechanism. |
| Outcome: | The proposed framework outperforms standard and attention-based CNN models on four different tasks. |
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Adaptive Convolution for Text Classification (N19-1)
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| Challenge: | Existing convolutional neural networks (CNNs) use sparse representations of text, such as bag-of-words. |
| Approach: | They propose an adaptive convolution for text classification to give flexibility to convolutional neural networks (CNNs) they attach filter-generating networks to convevolution blocks in existing CNNs . |
| Outcome: | The proposed convolution improves performance in seven benchmark datasets by 2.6 percentage points . the proposed conversions can be likened to players of the twenty questions . |
Convolutional Neural Networks with Recurrent Neural Filters (D18-1)
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| Challenge: | Convolutional neural networks (CNNs) use recurrent neural networks as convolution filters to capture language compositionality and long-term dependencies. |
| Approach: | They propose to use recurrent neural networks (RNNs) as convolution filters to capture language compositionality and long-term dependencies. |
| Outcome: | The proposed convolutional neural networks achieve state-of-the-art on two sentences and the Stanford Sentiment Treebank. |
Contextualized Word Representations for Reading Comprehension (N18-2)
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| Challenge: | Reading comprehension (RC) is a high-level task in natural language understanding that requires reading a document and answering questions about its content. |
| Approach: | They propose to provide a standard neural network for reading a document and answering a question about its content. |
| Outcome: | The proposed model improves on the competitive SQuAD dataset by providing rich contextualized word representations and allowing it to choose between context-dependent and context-independent representations. |
Contextualized and Generalized Sentence Representations by Contrastive Self-Supervised Learning: A Case Study on Discourse Relation Analysis (2021.naacl-main)
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| Challenge: | Existing methods to learn contextualized and generalized sentence representations are limited by the size of manually annotated data. |
| Approach: | They propose a method to learn contextualized and generalized sentence representations using contrastive self-supervised learning. |
| Outcome: | The proposed method outperforms baseline methods based on BERT, XLNet, and RoBERTa in English and Japanese and outperformed strong baseline methods. |
Structured Minimally Supervised Learning for Neural Relation Extraction (N19-1)
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| Challenge: | Recent work shows that distant supervision can cause significant label noise when learning from large quantities of unlabeled text. |
| Approach: | They propose a method that combines the benefits of learning representations and structured learning to predict sentence-level relation mentions given only proposition-level supervision from a KB. |
| Outcome: | The proposed approach outperforms a number of baseline approaches while minimizing label noise. |
Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings (P18-1)
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| Challenge: | recurrent neural networks have produced significant advances in part-of-speech tagging accuracy . a common feature of these models is the presence of rich initial word encodings . however, word or sub-word information interacts only through subsequent recursive layers . |
| Approach: | They propose to use recurrent neural networks with sentence-level context for initial character and word-based representations. |
| Outcome: | The proposed model has the highest accuracy of all participating systems in the CoNLL 2017 task. |
A Deep Neural Network Sentence Level Classification Method with Context Information (D18-1)
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| Challenge: | Existing methods that use context for sentence classification are difficult to scale . Usually, sentences are treated as separate instances for the task . however, in many situations the sentence that is the focus of classification appears in a context that can provide additional information. |
| Approach: | They propose a method that uses potentially large contexts to classify sentences . they use an LSTM, and short-span features to classize sentences based on a stacked CNN . |
| Outcome: | The proposed method consistently improves on two different datasets. |
Dynamic Meta-Embeddings for Improved Sentence Representations (D18-1)
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| Challenge: | A sprawling literature has emerged about what word embeddings are most useful for which tasks . word embed-ding is a technique that can be used to learn word-level meaning representations for a variety of tasks. |
| Approach: | They propose a method for supervised learning of embedding ensembles that leads to state-of-the-art performance on a variety of tasks. |
| Outcome: | The proposed method leads to state-of-the-art performance on a variety of tasks. |
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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A Regularization Approach for Incorporating Event Knowledge and Coreference Relations into Neural Discourse Parsing (D19-1)
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| Challenge: | Existing approaches to discourse parsing use commonsense knowledge and linguistic constraints to integrate them into neural network models. |
| Approach: | They propose a knowledge regularization approach that integrates linguistic constraints with contexts for deriving word representations. |
| Outcome: | The proposed approach outperforms previous systems on the benchmark dataset PDTB for discourse parsing. |