Papers with component models
A Hybrid Neural Network Model for Commonsense Reasoning (D19-60)
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| Challenge: | a hybrid neural network (HNN) model for commonsense reasoning is proposed . it combines language models and semantic similarity models to achieve new state-of-the-art results . |
| Approach: | They propose a hybrid neural network model for commonsense reasoning . it combines a masked language model and a semantic similarity model . |
| Outcome: | The proposed model outperforms the WNLI, WSC and PDP60 benchmarks on three commonsense reasoning tasks. |
ConvLab: Multi-Domain End-to-End Dialog System Platform (P19-3)
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Sungjin Lee, Qi Zhu, Ryuichi Takanobu, Zheng Zhang, Yaoqin Zhang, Xiang Li, Jinchao Li, Baolin Peng, Xiujun Li, Minlie Huang, Jianfeng Gao
| Challenge: | ConvLab is an open-source multi-domain end-to-end dialog system platform . it allows researchers to quickly set up experiments with reusable components and compare a large set of different approaches in common environments. |
| Approach: | They propose to use an open-source multi-domain end-to-end dialog system platform to train and evaluate dialog bots in common environments. |
| Outcome: | The proposed system enables researchers to quickly set up experiments with reusable components and compare a large set of different approaches in common environments. |
Stacking with Auxiliary Features for Visual Question Answering (N18-1)
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| Challenge: | Visual Question Answering (VQA) is a challenging task that requires systems to reason about natural language and vision. |
| Approach: | They propose four categories of auxiliary features for ensembling for VQA . three out of the four categories can be inferred from an image-question pair . fourth category uses model-specific explanations . |
| Outcome: | The proposed techniques improve performance for visual question answering (VQA) given an image and a natural language question, the task is to provide an accurate natural language answer. |
Boosted Dense Retriever (2022.naacl-main)
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| Challenge: | DrBoost is a dense retrieval ensemble that is trained in stages to correct retrieval mistakes . it produces representations which are 4x more compact, while delivering comparable retrieval results. |
| Approach: | They propose a dense retrieval ensemble inspired by boosting that is trained in stages . they produce representations which are 4x more compact, while delivering comparable retrieval results . |
| Outcome: | The proposed model performs surprisingly well under approximate search with coarse quantization, reducing latency and bandwidth needs by another 4x. |