Papers with Stanford Sentiment Treebank
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. |
Evaluation of Deep Gaussian Processes for Text Classification (2020.lrec-1)
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| Challenge: | Existing models for text classification are limited by the expressability limit and require enormous empirical efforts to come up with a robust model architecture. |
| Approach: | They propose a Bayesian non-parametric Bayessian nonparametric model with strong function compositionality for the task of Text Classification. |
| Outcome: | The proposed models outperform shallow and deep Gaussian processes on the TREC (Text REtrieval Conference) datasets. |
Tree Communication Models for Sentiment Analysis (P19-1)
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| Challenge: | Existing methods for sentiment classification over hierarchical phrases capture only bottom-up dependencies between constituents. |
| Approach: | They propose a tree-based sentiment analysis model using graph convolutional neural network and graph recurrent neural network which allows rich information exchange between phrases constituent tree. |
| Outcome: | The proposed model outperforms existing tree-LSTMs in accuracy and efficiency, providing more consistent predictions on phrase-level sentiments. |
Investigating Dynamic Routing in Tree-Structured LSTM for Sentiment Analysis (D19-1)
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| Challenge: | Existing deep neural network models such as LSTM and tree-LSTM have a bias problem where the words in the tail of a sentence are more heavily emphasized than those in the header. |
| Approach: | They propose a capsule tree-LSTM model that uses dynamic routing to build sentence representations by assigning different weights to nodes according to their contributions to prediction. |
| Outcome: | The proposed model improves on the Stanford Sentiment Treebank and EmoBank datasets. |
Latent Variable Sentiment Grammar (P19-1)
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| Challenge: | Existing neural models do not explicitly model sentiment composition, which requires to encode sentiment class labels. |
| Approach: | They propose a sentiment grammar that captures sentiment subtype expressions by latent variables and Gaussian mixture vectors. |
| Outcome: | The proposed model outperforms vanilla neural encoders on the Stanford Sentiment Treebank benchmark. |
Textual Data Augmentation for Efficient Active Learning on Tiny Datasets (2020.emnlp-main)
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| Challenge: | Existing active learning approaches for textual data are limited due to the complexity of language. |
| Approach: | They propose an approach where guided outputs of a language generation model can be enhanced through an active learning process. |
| Outcome: | The proposed approach achieves performance increases of 3% and 5% on TREC-6 and SST-2 datasets compared with NGDG, which does not optimize for a reward function. |