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20192022
most citedS^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization

765 citations · 982 across the 25 of their papers we have counts for

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9 papers · 1 filter

cs.CL2022★ 3 cited

InfoCSE: Information-aggregated Contrastive Learning of Sentence Embeddings

Xing Wu, Chaochen Gao, Zijia Lin +3

Contrastive learning has been extensively studied in sentence embedding learning, which assumes that the embeddings of different views of the same sentence are closer. The constrai…

cs.CL2022★ 8 cited

ConTextual Masked Auto-Encoder for Dense Passage Retrieval

Xing Wu, Guangyuan Ma, Meng Lin +3

Dense passage retrieval aims to retrieve the relevant passages of a query from a large corpus based on dense representations (i.e., vectors) of the query and the passages. Recent s…

cs.CL2022★ 1 cited

Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification Tasks

Xing Wu, Chaochen Gao, Meng Lin +3

Before entering the neural network, a token is generally converted to the corresponding one-hot representation, which is a discrete distribution of the vocabulary. Smoothed represe…

cs.CL2021★ 69 cited

ESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence Embedding

Xing Wu, Chaochen Gao, Liangjun Zang +3

Contrastive learning has been attracting much attention for learning unsupervised sentence embeddings. The current state-of-the-art unsupervised method is the unsupervised SimCSE (…

cs.CL2021★ 9 cited

Smoothed Contrastive Learning for Unsupervised Sentence Embedding

Xing Wu, Chaochen Gao, Yipeng Su +3

Contrastive learning has been gradually applied to learn high-quality unsupervised sentence embedding. Among the previous un-supervised methods, the latest state-of-the-art method,…

cs.CL2021

Converse, Focus and Guess -- Towards Multi-Document Driven Dialogue

Han Liu, Caixia Yuan, Xiaojie Wang +3

We propose a novel task, Multi-Document Driven Dialogue (MD3), in which an agent can guess the target document that the user is interested in by leading a dialogue. To benchmark pr…

cs.CL2020★ 2 cited

Combining Self-Training and Self-Supervised Learning for Unsupervised Disfluency Detection

Shaolei Wang, Zhongyuan Wang, Wanxiang Che +1

Most existing approaches to disfluency detection heavily rely on human-annotated corpora, which is expensive to obtain in practice. There have been several proposals to alleviate t…

cs.CL2020

Query-aware Tip Generation for Vertical Search

Yang Yang, Junmei Hao, Canjia Li +7

As a concise form of user reviews, tips have unique advantages to explain the search results, assist users' decision making, and further improve user experience in vertical search…

cs.CL2019★ 7 cited

Earlier Attention? Aspect-Aware LSTM for Aspect-Based Sentiment Analysis

Bowen Xing, Lejian Liao, Dandan Song +4

Aspect-based sentiment analysis (ABSA) aims to predict fine-grained sentiments of comments with respect to given aspect terms or categories. In previous ABSA methods, the importanc…