activity
20202025
most citedLocal Citation Recommendation with Hierarchical-Attention Text Encoder and SciBERT-based Reranking

1 citations · 1 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CL2025

SpiritRAG: A Q&A System for Religion and Spirituality in the United Nations Archive

Yingqiang Gao, Fabian Winiger, Patrick Montjourides +4

Religion and spirituality (R/S) are complex and highly domain-dependent concepts which have long confounded researchers and policymakers. Due to their context-specificity, R/S are…

cs.HC2024

MODOC: A Modular Interface for Flexible Interlinking of Text Retrieval and Text Generation Functions

Yingqiang Gao, Jhony Prada, Nianlong Gu +2

Large Language Models (LLMs) produce eloquent texts but often the content they generate needs to be verified. Traditional information retrieval systems can assist with this task, b…

cs.CL2023

MemSum-DQA: Adapting An Efficient Long Document Extractive Summarizer for Document Question Answering

Nianlong Gu, Yingqiang Gao, Richard H. R. Hahnloser

We introduce MemSum-DQA, an efficient system for document question answering (DQA) that leverages MemSum, a long document extractive summarizer. By prefixing each text block in the…

cs.CL2023

Unsupervised Scientific Abstract Segmentation with Normalized Mutual Information

Yingqiang Gao, Jessica Lam, Nianlong Gu +1

The abstracts of scientific papers consist of premises and conclusions. Structured abstracts explicitly highlight the conclusion sentences, whereas non-structured abstracts may hav…

cs.IR2021★ 1 cited

Local Citation Recommendation with Hierarchical-Attention Text Encoder and SciBERT-based Reranking

Nianlong Gu, Yingqiang Gao, Richard H. R. Hahnloser

The goal of local citation recommendation is to recommend a missing reference from the local citation context and optionally also from the global context. To balance the tradeoff b…

cs.CL2020

Character-Level Translation with Self-attention

Yingqiang Gao, Nikola I. Nikolov, Yuhuang Hu +1

We explore the suitability of self-attention models for character-level neural machine translation. We test the standard transformer model, as well as a novel variant in which the…