Songlin Hu
6 papers in the PaperMetrix corpus
Papers by this author
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A Span-based Joint Model for Opinion Target Extraction and Target Sentiment Classification
2019
Target-Based Sentiment Analysis aims at extracting opinion targets and classifying the sentiment polarities expressed on each target. Recently, token based sequence tagging methods have been successfully applied to jointly solve the two tasks, which aims …
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Pre-training with Large Language Model-based Document Expansion for Dense Passage Retrieval
2023 · arXiv (Cornell University)
In this paper, we systematically study the potential of pre-training with Large Language Model(LLM)-based document expansion for dense passage retrieval. Concretely, we leverage the capabilities of LLMs for document expansion, i.e. query generation, and effectively …
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Dial-MAE: ConTextual Masked Auto-Encoder for Retrieval-based Dialogue Systems
2024
Zhenpeng Su, Xing W, Wei Zhou, Guangyuan Ma, Songlin Hu. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.
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Symmetric Metric Learning with Adaptive Margin for Recommendation
2020 · Proceedings of the AAAI Conference on Artificial Intelligence
Metric learning based methods have attracted extensive interests in recommender systems. Current methods take the user-centric way in metric space to ensure the distance between user and negative item to be larger than that between …
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Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering
2020 · Proceedings of the AAAI Conference on Artificial Intelligence
Commonsense question answering aims to answer questions which require background knowledge that is not explicitly expressed in the question. The key challenge is how to obtain evidence from external knowledge and make predictions based on …
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ESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence Embedding
2021 · arXiv (Cornell University)
Contrastive learning has been attracting much attention for learning unsupervised sentence embeddings. The current state-of-the-art unsupervised method is the unsupervised SimCSE (unsup-SimCSE). Unsup-SimCSE takes dropout as a minimal data augmentation method, and passes the same …