Papers by Shijing Si
Methods for Numeracy-Preserving Word Embeddings (2020.emnlp-main)
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Dhanasekar Sundararaman, Shijing Si, Vivek Subramanian, Guoyin Wang, Devamanyu Hazarika, Lawrence Carin
| Challenge: | Word embedding models capture semantic relationships between words but fail to capture numerical properties associated with numbers. |
| Approach: | They propose a method to assign and learn embeddings for numbers using word embedders. |
| Outcome: | The proposed model outperforms pre-trained word embedding models across multiple examples of two tasks. |
Integrating Task Specific Information into Pretrained Language Models for Low Resource Fine Tuning (2020.findings-emnlp)
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| Challenge: | Existing pretrained language models are agnostic to downstream information and can overfit when fine-tuned with low resource datasets. |
| Approach: | They integrate label information as a task-specific prior into the self-attention component of pretrained BERT models. |
| Outcome: | Experiments on benchmarks and real-word datasets show that the proposed approach can improve the performance of pretrained models when fine-tuned with small datasets. |
Efficient Document Retrieval by End-to-End Refining and Quantizing BERT Embedding with Contrastive Product Quantization (2022.emnlp-main)
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| Challenge: | Existing semantic hashing methods only learn a binary code for each document and use Hamming distance to evaluate document distances. |
| Approach: | They propose to leverage BERT embeddings to perform efficient retrieval based on product quantization technique . they transform original BERT embedded codewords and feed it into a probabilistic product quantizer module . |
| Outcome: | The proposed method outperforms current state-of-the-art methods on three benchmarks. |
Leveraging BERT and TFIDF Features for Short Text Clustering via Alignment-Promoting Co-Training (2024.emnlp-main)
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| Challenge: | Existing clustering methods rely on keyword information, but they lack this information. |
| Approach: | They propose a CO**-**T**raining **C**lustering framework to make use of BERT and TFIDF features. |
| Outcome: | The proposed framework outperforms existing SOTA methods on eight datasets. |