Papers by Huaishao Luo
Hashing based Efficient Inference for Image-Text Matching (2021.findings-acl)
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| Challenge: | Recent work on image-text matching has focused on exploring interactions between images and sentences to improve performance without considering inference efficiency. |
| Approach: | They propose a hashing-based efficient inference module which can be plugged into existing frameworks to speed up inference step without reducing retrieval performance. |
| Outcome: | The proposed module can be plugged into existing framework to speed up inference step without reducing retrieval performance. |
GEM: A General Evaluation Benchmark for Multimodal Tasks (2021.findings-acl)
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Lin Su, Nan Duan, Edward Cui, Lei Ji, Chenfei Wu, Huaishao Luo, Yongfei Liu, Ming Zhong, Taroon Bharti, Arun Sacheti
| Challenge: | Existing datasets that focus on natural language tasks are not considered as a general evaluation benchmark for multimodal tasks. |
| Approach: | They present a general evaluation benchmark for multimodal tasks, GEM 1 . they compare it with existing multimodal vision-language datasets . |
| Outcome: | The proposed model is compared with existing vision-language datasets focusing on natural language tasks . it is the largest vision-linguistic dataset covering image-language tasks and video-language task at the same time . |
DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction (P19-1)
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| Challenge: | Existing algorithms address aspect term extraction and aspect sentiment classification as separate tasks, which can be complicated for real applications. |
| Approach: | They propose a dual crOss-sharEd RNN framework to generate all aspect term-polarity pairs of the input sentence simultaneously. |
| Outcome: | The proposed framework outperforms state-of-the-art frameworks on three benchmark datasets. |
Control Image Captioning Spatially and Temporally (2021.acl-long)
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| Challenge: | Existing methods to generate image captions with user intention are still under exploration. |
| Approach: | They propose a model that connects Contrastive constraints and Attention Guidance in a loop manner and engages explicit spatial and temporal constraints to the generating process. |
| Outcome: | The proposed model improves performance on a trace-controlled image captioning task. |
GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment Analysis (2020.findings-emnlp)
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| Challenge: | Existing studies ignore aspect terms interaction when labeling polarities . aspect terms extraction and aspect sentiment classification are two fundamental tasks . |
| Approach: | They propose a GRadient hArmonized and CascadEd labeling model to solve the imbalance issue . they extend the gradient harmonized mechanism used in object detection to aspect-based sentiment analysis . |
| Outcome: | The proposed model achieves consistency improvement on multiple benchmark datasets and generates state-of-the-art results. |
CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking (2022.aacl-short)
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| Challenge: | Existing few-shot dialogue state tracking (DST) methods transfer knowledge from labeled data into DST, but collecting large amount of labeles is laborious. |
| Approach: | They propose a few-shot dialogue state tracking framework that integrates self-training and self-supervised learning methods into the framework. |
| Outcome: | The proposed framework achieves competitive performance in several few-shot scenarios. |
MaP: A Matrix-based Prediction Approach to Improve Span Extraction in Machine Reading Comprehension (2020.aacl-main)
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| Challenge: | Existing methods to predict the start and end positions of answer spans generate two probability vectors. |
| Approach: | They propose a method that extends the probability vector to a probability matrix. |
| Outcome: | The proposed method improves on SQuAD 1.1 and three other question answering benchmarks. |