575 citations · 1.6k across the 40 of their papers we have counts for
13 papers · 1 filter
Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision
Si Sun, Yingzhuo Qian, Zhenghao Liu +5
The effectiveness of Neural Information Retrieval (Neu-IR) often depends on a large scale of in-domain relevance training signals, which are not always available in real-world rank…
CMT in TREC-COVID Round 2: Mitigating the Generalization Gaps from Web to Special Domain Search
Chenyan Xiong, Zhenghao Liu, Si Sun +7
Neural rankers based on deep pretrained language models (LMs) have been shown to improve many information retrieval benchmarks. However, these methods are affected by their the cor…
Towards Interpretable Natural Language Understanding with Explanations as Latent Variables
Wangchunshu Zhou, Jinyi Hu, Hanlin Zhang +4
Recently generating natural language explanations has shown very promising results in not only offering interpretable explanations but also providing additional information and sup…
Long Document Ranking with Query-Directed Sparse Transformer
Jyun-Yu Jiang, Chenyan Xiong, Chia-Jung Lee +1
The computing cost of transformer self-attention often necessitates breaking long documents to fit in pretrained models in document ranking tasks. In this paper, we design Query-Di…
Text Classification Using Label Names Only: A Language Model Self-Training Approach
Yu Meng, Yunyi Zhang, Jiaxin Huang +4
Current text classification methods typically require a good number of human-labeled documents as training data, which can be costly and difficult to obtain in real applications. H…
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
Lee Xiong, Chenyan Xiong, Ye Li +5
Conducting text retrieval in a dense learned representation space has many intriguing advantages over sparse retrieval. Yet the effectiveness of dense retrieval (DR) often requires…
Knowledge-Aware Language Model Pretraining
Corby Rosset, Chenyan Xiong, Minh Phan +3
How much knowledge do pretrained language models hold? Recent research observed that pretrained transformers are adept at modeling semantics but it is unclear to what degree they g…
Proceedings of the KG-BIAS Workshop 2020 at AKBC 2020
Edgar Meij, Tara Safavi, Chenyan Xiong +3
The KG-BIAS 2020 workshop touches on biases and how they surface in knowledge graphs (KGs), biases in the source data that is used to create KGs, methods for measuring or remediati…
Few-Shot Generative Conversational Query Rewriting
Shi Yu, Jiahua Liu, Jingqin Yang +4
Conversational query rewriting aims to reformulate a concise conversational query to a fully specified, context-independent query that can be effectively handled by existing inform…
Capturing Global Informativeness in Open Domain Keyphrase Extraction
Si Sun, Zhenghao Liu, Chenyan Xiong +2
Open-domain KeyPhrase Extraction (KPE) aims to extract keyphrases from documents without domain or quality restrictions, e.g., web pages with variant domains and qualities. Recentl…
TREC CAsT 2019: The Conversational Assistance Track Overview
Jeffrey Dalton, Chenyan Xiong, Jamie Callan
The Conversational Assistance Track (CAsT) is a new track for TREC 2019 to facilitate Conversational Information Seeking (CIS) research and to create a large-scale reusable test co…
Selective Weak Supervision for Neural Information Retrieval
Kaitao Zhang, Chenyan Xiong, Zhenghao Liu +1
This paper democratizes neural information retrieval to scenarios where large scale relevance training signals are not available. We revisit the classic IR intuition that anchor-do…
TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network
Jiaming Shen, Zhihong Shen, Chenyan Xiong +3
Taxonomies consist of machine-interpretable semantics and provide valuable knowledge for many web applications. For example, online retailers (e.g., Amazon and eBay) use taxonomies…