Papers by Feilong Chen
DualGATs: Dual Graph Attention Networks for Emotion Recognition in Conversations (2023.acl-long)
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| Challenge: | Existing studies focus on speaker-aware context modeling, overlooking the discourse structure of the conversation. |
| Approach: | They propose Dual Graph ATtention networks to capture contextual dependencies in conversational contexts and integrate it into a speaker-aware GAT module. |
| Outcome: | The proposed model outperforms state-of-the-art models on four datasets and is highly efficient. |
Unsupervised Knowledge Selection for Dialogue Generation (2021.findings-acl)
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| Challenge: | Existing knowledge selection tasks require the preidentified knowledge to generate informative dialogues. |
| Approach: | They propose a novel method to supervise knowledge selection when the gold knowledge label is unknown by obtaining an oracle knowledge label via distant supervision and leverage knowledge distillation to alleviate the noisy labeling problem of distant supervision. |
| Outcome: | The proposed method outperforms strong supervised baselines on two knowledge-grounded dialogue datasets and generates more informative responses. |
Learning to Ground Visual Objects for Visual Dialog (2021.findings-emnlp)
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| Challenge: | Existing methods to ground visual objects are inadequate for visual dialog . a posterior distribution is inferred from context and questions, while posterior distributions are used to facilitate visual objects grounding. |
| Approach: | They propose a method to learn to ground visual objects for visual dialog using prior and posterior distributions over visual objects to facilitate visual objects grounding. |
| Outcome: | The proposed approach improves the existing models in generative and discriminative settings by a significant margin. |
Multimodal Incremental Transformer with Visual Grounding for Visual Dialogue Generation (2021.findings-acl)
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| Challenge: | Existing studies focus on implicit exploration of multimodal coreference but neglect the importance of locating the objects explicitly in the visual content, which is associated with textual entities. |
| Approach: | They propose a multimodal incremental transformer with visual grounding which aims to explicitly locate related objects in the image guided by textual entities. |
| Outcome: | The proposed model achieves comparable performance on the VisDial v0.9 and v1.0 datasets. |
Hyperbolic Representations for Prompt Learning (2024.lrec-main)
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| Challenge: | Existing techniques to train only continuous prompts while freezing the language model have been developed. |
| Approach: | They propose to use hyperbolic space to model hierarchical relationships between prompts and inputs . they use a Poincaré disk to capture the hierarchic relationship between prompt and input . |
| Outcome: | The proposed approach reduces training time and storage for downstream tasks by reducing training costs. |
GoG: Relation-aware Graph-over-Graph Network for Visual Dialog (2021.findings-acl)
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| Challenge: | Experimental results show that our model outperforms the strong baseline in both generative and discriminative settings by a significant margin. |
| Approach: | They propose a relation-aware graph-over-graph network (GoG) for visual dialog . their model outperforms the strong baseline in both generative and discriminative settings . |
| Outcome: | The proposed model outperforms baseline models in both generative and discriminative settings by a significant margin. |
Bridging the Gap between Prior and Posterior Knowledge Selection for Knowledge-Grounded Dialogue Generation (2020.emnlp-main)
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| Challenge: | Existing knowledge-grounded dialogue models lack prior and posterior knowledge selection . prior selection module may not learn to select knowledge properly because of lack of posterior information . |
| Approach: | They propose a knowledge distillation-based training strategy to remove the exposure bias of knowledge selection. |
| Outcome: | The proposed model improves on two knowledge-grounded dialogue datasets. |