Papers with Long Short-Term Memory
Restricted Recurrent Neural Tensor Networks: Exploiting Word Frequency and Compositionality (P18-2)
Copied to clipboard
| Challenge: | Recurrent neural tensor networks (RNNs) increase capacity by augmenting the size of the hidden layer, with significant increase in computational cost. |
| Approach: | They propose restricted recurrent neural tensor networks (r-RNTNs) which reserve distinct hidden layer weights for frequent vocabulary words while sharing a single set of weights . |
| Outcome: | The proposed model outperforms unrestricted RNTNs using only a small fraction of the parameters of unrestrained RNNNs. |
Building a De-identification System for Real Swedish Clinical Text Using Pseudonymised Clinical Text (D19-62)
Copied to clipboard
| Challenge: | Several ethical and scientific issues arise regarding the balance between maintaining patient confidentiality and the need for wider application of trained models. |
| Approach: | They propose to use pseudonymised clinical text as training data to de-identify real clinical text in other hospitals. |
| Outcome: | The proposed model performed better for some PHI information than the standard model and poor performance on Location and Health Care Unit information. |
Mixed Feelings: Natural Text Generation with Variable, Coexistent Affective Categories (P18-3)
Copied to clipboard
| Challenge: | a recent study has shown that language models which can generate emotional sentences are limited to one affective category out of a few. |
| Approach: | a new research proposal proposes a language model which can produce multiple emotions simultaneously. authors propose to use a long-term memory language model to allow for variation in multiple emotions. |
| Outcome: | a new language model allows for variation in multiple emotions simultaneously . the proposed model is based on a model of long-term memory . |
Effective Attention Modeling for Aspect-Level Sentiment Classification (C18-1)
Copied to clipboard
| Challenge: | Aspect-level sentiment classification aims to determine sentiment polarity of review sentence towards opinion target . main challenge is to separate different opinion contexts for different targets . |
| Approach: | They propose a method that captures the semantic meaning of the opinion target and a model that incorporates syntactic information into the attention mechanism. |
| Outcome: | The proposed method captures the semantic meaning of the opinion target and incorporates syntactic information into the attention mechanism. |
Modeling Human Sentence Processing with Left-Corner Recurrent Neural Network Grammars (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing literature is agnostic about a parsing strategy of hierarchical models . a recent study showed that hierarchically model hierarchic structures capture grammatical dependencies much better than RNNs in targeted syntactic evaluations. |
| Approach: | They evaluated three LMs with head-final left-branching structures and Recurrent Neural Network Grammars with top-down and left-corner parsing strategies as hierarchical models. |
| Outcome: | The proposed model outperforms top-down and left-corner models against human reading times in Japanese. |
Sound Signal Processing with Seq2Tree Network (L18-1)
Copied to clipboard
| Challenge: | Recent LSTM models have been used to model sequential data processing tasks because of their ability to preserve previous information weighted on distance. |
| Approach: | They propose to use a tree-structured tree-based neural network architecture to solve the problem of unbalanced connections between data units inside and outside semantic groups. |
| Outcome: | The proposed model outperforms the state-of-the-art Bidirectional LSTM model on a signal and noise separation task. |
Bridging Robustness and Generalization Against Word Substitution Attacks in NLP via the Growth Bound Matrix Approach (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent studies have shown that adversarial examples can alter models' predicted sentiment due to their sensitivity to specific word choices. |
| Approach: | They propose a regularization technique to improve NLP model robustness by reducing the impact of input perturbations on model outputs. |
| Outcome: | The proposed method outperforms state-of-the-art methods in adversarial defense. |