Papers by Fangxiang Feng
COM-MRC: A COntext-Masked Machine Reading Comprehension Framework for Aspect Sentiment Triplet Extraction (2022.emnlp-main)
Copied to clipboard
| Challenge: | Aspect Sentiment Triplet Extraction (ASTE) aims to extract sentiment triplets from sentences, but when faced with multiple aspect terms, the MRC-based methods could fail due to the interference from other aspect terms. |
| Approach: | They propose a COntext-Masked MRC framework for Aspect Sentiment Triplet Extraction (ASTE) which aims to extract sentiment triplets from sentences . |
| Outcome: | The proposed framework outperforms state-of-the-art methods on benchmark datasets and shows that it can extract sentiment triplets from multiple aspect terms. |
Dual Graph Convolutional Networks for Aspect-based Sentiment Analysis (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods to model relationships between aspects and opinion words are inefficient due to informal expressions and complexity of online reviews. |
| Approach: | They propose a dual graph convolutional networks model that considers complementarity of syntax structures and semantic correlations simultaneously. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three public datasets and validates it. |
Multi-stage Pre-training over Simplified Multimodal Pre-training Models (2021.acl-long)
Copied to clipboard
| Challenge: | Existing multimodal pre-training models require large amounts of training data and have huge model sizes, making them impossible to apply in low-resource situations. |
| Approach: | They propose a multi-stage pre-training method which uses information at different granularities from word, phrase to sentence in both texts and images to pre-train a model in stages. |
| Outcome: | The proposed method outperforms the original model in Image-Text Retrieval task and outperformed the original LXMERT model in downstream tasks. |
Co-VQA : Answering by Interactive Sub Question Sequence (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to Visual Question Answering (VQA) answer questions directly, but people usually decompose a complex question into a sequence of simple sub questions. |
| Approach: | They propose a conversation-based VQA framework that decomposes questions into sub questions and answers them one-by-one. |
| Outcome: | The proposed framework achieves state-of-the-art on VQA 2.0 and VQA-CP v2 datasets. |
Enhanced Multi-Channel Graph Convolutional Network for Aspect Sentiment Triplet Extraction (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods to extract aspect triplets ignore the relationships between words . Enhanced Multi-Channel Graph Convolutional Network model can be used to learn relation-aware node representations. |
| Approach: | They propose an Enhanced Multi-Channel Graph Convolutional Network model to fully utilize the relations between words for ASTE task. |
| Outcome: | The proposed model outperforms state-of-the-art methods significantly on a benchmark dataset. |
A Simple Model for Distantly Supervised Relation Extraction (2022.coling-1)
Copied to clipboard
| Challenge: | Recent methods focus on exploiting bag representations with complex de-noising scheme to achieve remarkable performance. |
| Approach: | They propose a BERT-based Graph convolutional network model that exploits bag representations . their model extracts key information from each instance and constructs a bag graph . |
| Outcome: | The proposed model improves on two benchmark datasets, i.e., NYT10 and GDS. |