Papers by Jianhui Jiang
A Domain Knowledge Enhanced Pre-Trained Language Model for Vertical Search: Case Study on Medicinal Products (2022.coling-1)
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| Challenge: | Existing pre-trained language models lack medicinal product knowledge for product vertical search. |
| Approach: | They propose a biomedical knowledge enhanced pre-trained language model for medicinal product vertical search using ELECTRA’s replaced token detection (RTD) pre-training. |
| Outcome: | The proposed model improves query-title relevance, query intent classification, and named entity recognition in query. |
Automatic Keyphrase Generation by Incorporating Dual Copy Mechanisms in Sequence-to-Sequence Learning (2022.coling-1)
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| Challenge: | Existing models for keyphrase generation use a copy mechanism to generate keyphrases, but they do not identify key words in the source text and copy them to create more keyphrase. |
| Approach: | They propose a dual-copier keyphrase generation model that uses a sequence-to-sequence model to generate keyphrases for a piece of text. |
| Outcome: | The proposed model outperforms baseline models and achieves an obvious performance improvement. |
Integrating Group-based Preferences from Coarse to Fine for Cold-start Users Recommendation (2025.coling-main)
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| Challenge: | Existing approaches to cross-domain recommendation (CDR) draw on historical purchase records or reviews to generate user representations. |
| Approach: | They propose a model that integrates preferences from coarse to fine levels to improve recommendations for cold-start users. |
| Outcome: | The proposed model outperforms state-of-the-art approaches on three CDR tasks. |
A Hierarchical Sequence-to-Set Model with Coverage Mechanism for Aspect Category Sentiment Analysis (2024.lrec-main)
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| Challenge: | Aspect category sentiment analysis (ACSA) aims to detect aspect categories and their corresponding sentiment polarities (category-sentiment pairs) generative models face three challenges, including addressing the missing predictions and focusing on relevant sentiment words. |
| Approach: | They propose to use sequence-to-set learning to tackle all three challenges simultaneously. |
| Outcome: | The proposed model is able to detect aspect categories and their corresponding sentiment polarities (category-sentiment pairs) but it is unable to predict all aspect categories within a sentence due to the disordered set. |
Alleviating Over-smoothing for Unsupervised Sentence Representation (2023.acl-long)
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| Challenge: | Existing approaches to learn better unsupervised sentence representations have been successful . over-smoothing problem in unsupervised sentences reduces the capacity of powerful PLMs . |
| Approach: | They propose a method to solve the over-smoothing problem in unsupervised sentence representations by combining negatives from PLMs intermediate layers. |
| Outcome: | The proposed method improves on different strong baselines on Semantic Textual Similarity and Transfer datasets. |
Structural Contrastive Pretraining for Cross-Lingual Comprehension (2023.findings-acl)
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| Challenge: | Existing methods to train multilingual language models using pretraining tasks like mask language modeling have yielded promising results on a wide range of downstream tasks. |
| Approach: | They propose a new task to align the structural words in a parallel sentence, enhancing models’ ability to comprehend cross-lingual representations. |
| Outcome: | The proposed task improves model's ability to comprehend cross-lingual representations by increasing the frequency of negative pairings. |