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- Investigating Capsule Networks with Dynamic Routing for Text Classification392
This work proposes three strategies to stabilize the dynamic routing process to alleviate the disturbance of some noise capsules which may contain “background” information or have not been successfully trained.
- A Challenge Dataset and Effective Models for Aspect-Based Sentiment Analysis344
A new large-scale Multi-Aspect Multi-Sentiment (MAMS) dataset, in which each sentence contains at least two different aspects with different sentiment polarities, and proposes simple yet effective CapsNet and CapsNet-BERT models which combine the strengths of recent NLP advances.
- Large Language Models are Versatile Decomposers: Decomposing Evidence and Questions for Table-based Reasoning288
This work exploits large language models (LLMs) as decomposers for effective table-based reasoning, and proposes a novel ''parsing-execution-filling'' strategy to decompose a complex question into simper step-by-step sub-questions by generating intermediate SQL queries as a bridge.
- GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamic Networks221
A novel non-linear model GCN-GAN is introduced to tackle the challenging temporal link prediction task of weighted dynamic networks and achieves impressive results compared to the state-of-the-art competitors.
- Attention Based LSTM for Target Dependent Sentiment Classification207
An attention-based bidirectional LSTM approach to improve the target-dependent sentiment classification by learning the alignment between the target entities and the most distinguishing features.
- GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-supervised Learning and Explicit Policy Injection187
GALAXY is a novel pre-trained dialog model that explicitly learns dialog policy from limited labeled dialogs and large-scale unlabeled dialog corpora via semi-supervised learning and has a stronger few-shot ability than existing models under various low-resource settings.
- Generative Adversarial Network for Abstractive Text Summarization178
This paper builds the generator G as an agent of reinforcement learning, which takes the raw text as input and predicts the abstractive summarization, and builds a discriminator which attempts to distinguish the generated summary from the ground truth summary.
- A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers164
This survey provides a comprehensive review of existing deep learning techniques for Relation extraction and proposes a new taxonomy to categorize existing works from three perspectives, i.e., text representation, context encoding, and triplet prediction.
- Multi-Modal Sarcasm Detection with Interactive In-Modal and Cross-Modal Graphs157
This paper explores an interactive graph convolution network (GCN) structure to jointly and interactively learn the incongruity relations of in- modal and cross-modal graphs for determining the significant clues in sarcasm detection and demonstrates that the proposed model achieves state-of-the-art performance.
- Enhancing Cross-target Stance Detection with Transferable Semantic-Emotion Knowledge155
A Semantic-Emotion Knowledge Transferring (SEKT) model for cross-target stance detection is proposed, which uses the external knowledge (semantic and emotion lexicons) as a bridge to enable knowledge transfer across different targets.
- A Survey of Natural Language Generation149
This survey aims to give the latest synthesis of deep learning research on the NLG core tasks, as well as the architectures adopted in the field, and detail meticulously and comprehensively various NLG tasks and datasets.
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- Transition-based Directed Graph Construction for Emotion-Cause Pair Extraction108
A transition-based model is proposed to transform the emotion-cause pair extraction task into a procedure of parsing-like directed graph construction, from which it can recognize emotions with the corresponding causes simultaneously, thereby optimizing separate subtasks jointly and maximizing mutual benefits of tasks interdependently.
- Target-adaptive Graph for Cross-target Stance Detection103
A novel approach that automatically identifies and adapts the target-dependent and target-independent roles that a word plays with respect to a specific target in stance expressions, so as to achieve cross-target stance detection is presented.
- Zero-Shot Stance Detection via Contrastive Learning100
A novel hierarchical contrastive learning strategy is devised to capture the correlation and difference between target-invariant and -specific features and further among different stance labels that allows the model to exploit transferable stance features more effectively for representing the stance of previously unseen targets.
- An ontology-driven clinical decision support system (IDDAP) for infectious disease diagnosis and antibiotic prescription86
This study attempted to bridge the patient/caregiver gap by building a sophisticated application that uses artificial intelligence and machine learning computational techniques to perform data-driven decision-making at the point of primary care.
- Spectral Clustering of Large-scale Data by Directly Solving Normalized Cut79
A new optimization algorithm, namely Direct Normalized Cut (DNC), is proposed to directly optimize the normalized cut model and show the superior performance of BKM, DNC and FNC on large-scale data sets.
- Facial expression recognition with deep learning78
This paper proposes a new method for static facial expression recognition using the CNN model to classify a set of static images into 7 basic emotions and then achieve effective classification automatically.
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- Boundary-Driven Table-Filling for Aspect Sentiment Triplet Extraction70
Boundary-Driven Table-Filling (BDTF), which represents each triplet as a relation region in the 2D table and transforms the ASTE task into detection and classification of relation regions, is proposed.
- Scalable Normalized Cut with Improved Spectral Rotation70
In the clustering process, an improved spectral rotation method is proposed to obtain the solution of the final clustering assignments and the experimental results show the superior performance of the new Scalable Normalized Cut method.
- Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation69
SPACE is proposed, a novel unified pre-trained dialog model learning from large-scale dialog corpora with limited annotations, which can be effectively fine-tuned on a wide range of downstream dialog tasks.
- Investigating the transferring capability of capsule networks for text classification68
The transferring capability of the proposed cross-domain capsule network (TL-Capsule) is demonstrated by applying it to two transfer learning applications: single-label to multi-label text classification and cross- domain sentiment classification.
- Supervised Feature Selection With a Stratified Feature Weighting Method66
The proposed stratified feature ranking (SFR) method was compared with six feature selection methods on a set of high-dimensional datasets and the results show that SFR was superior to most of these feature selection method.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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