no code implementations • 13 Oct 2022 • Linqing Liu, Minghan Li, Jimmy Lin, Sebastian Riedel, Pontus Stenetorp
To balance these two considerations, we propose a combination of an effective filtering strategy and fusion of the retrieved documents based on the generation probability of each context.
1 code implementation • 10 Oct 2022 • Raphael Tang, Linqing Liu, Akshat Pandey, Zhiying Jiang, Gefei Yang, Karun Kumar, Pontus Stenetorp, Jimmy Lin, Ferhan Ture
Large-scale diffusion neural networks represent a substantial milestone in text-to-image generation, but they remain poorly understood, lacking interpretability analyses.
1 code implementation • 1 Feb 2022 • Jean Kaddour, Linqing Liu, Ricardo Silva, Matt J. Kusner
Recently, flat-minima optimizers, which seek to find parameters in low-loss neighborhoods, have been shown to improve a neural network's generalization performance over stochastic and adaptive gradient-based optimizers.
1 code implementation • Findings (NAACL) 2022 • Linqing Liu, Patrick Lewis, Sebastian Riedel, Pontus Stenetorp
Recent work on Open Domain Question Answering has shown that there is a large discrepancy in model performance between novel test questions and those that largely overlap with training questions.
1 code implementation • Findings (ACL) 2021 • Chien-Sheng Wu, Linqing Liu, Wenhao Liu, Pontus Stenetorp, Caiming Xiong
In this paper, we aim to improve abstractive dialogue summarization quality and, at the same time, enable granularity control.
1 code implementation • 13 Feb 2021 • Patrick Lewis, Yuxiang Wu, Linqing Liu, Pasquale Minervini, Heinrich Küttler, Aleksandra Piktus, Pontus Stenetorp, Sebastian Riedel
We introduce a new QA-pair retriever, RePAQ, to complement PAQ.
no code implementations • 1 Jan 2021 • Chien-Sheng Wu, Linqing Liu, Wenhao Liu, Pontus Stenetorp, Caiming Xiong
2) A simple strategy to control the granularity of the final summary.
no code implementations • 1 Jan 2021 • Sewon Min, Jordan Boyd-Graber, Chris Alberti, Danqi Chen, Eunsol Choi, Michael Collins, Kelvin Guu, Hannaneh Hajishirzi, Kenton Lee, Jennimaria Palomaki, Colin Raffel, Adam Roberts, Tom Kwiatkowski, Patrick Lewis, Yuxiang Wu, Heinrich Küttler, Linqing Liu, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel, Sohee Yang, Minjoon Seo, Gautier Izacard, Fabio Petroni, Lucas Hosseini, Nicola De Cao, Edouard Grave, Ikuya Yamada, Sonse Shimaoka, Masatoshi Suzuki, Shumpei Miyawaki, Shun Sato, Ryo Takahashi, Jun Suzuki, Martin Fajcik, Martin Docekal, Karel Ondrej, Pavel Smrz, Hao Cheng, Yelong Shen, Xiaodong Liu, Pengcheng He, Weizhu Chen, Jianfeng Gao, Barlas Oguz, Xilun Chen, Vladimir Karpukhin, Stan Peshterliev, Dmytro Okhonko, Michael Schlichtkrull, Sonal Gupta, Yashar Mehdad, Wen-tau Yih
We review the EfficientQA competition from NeurIPS 2020.
no code implementations • 9 Nov 2019 • Linqing Liu, Huan Wang, Jimmy Lin, Richard Socher, Caiming Xiong
Our approach is model agnostic and can be easily applied on different future teacher model architectures.
no code implementations • IJCNLP 2019 • Linqing Liu, Wei Yang, Jinfeng Rao, Raphael Tang, Jimmy Lin
Semantic similarity modeling is central to many NLP problems such as natural language inference and question answering.
no code implementations • IJCNLP 2019 • Jinfeng Rao, Linqing Liu, Yi Tay, Wei Yang, Peng Shi, Jimmy Lin
A core problem of information retrieval (IR) is relevance matching, which is to rank documents by relevance to a user{'}s query.
4 code implementations • 28 Mar 2019 • Raphael Tang, Yao Lu, Linqing Liu, Lili Mou, Olga Vechtomova, Jimmy Lin
In the natural language processing literature, neural networks are becoming increasingly deeper and complex.
Ranked #60 on Sentiment Analysis on SST-2 Binary classification
1 code implementation • 26 Nov 2017 • Linqing Liu, Yao Lu, Min Yang, Qiang Qu, Jia Zhu, Hongyan Li
In this paper, we propose an adversarial process for abstractive text summarization, in which we simultaneously train a generative model G and a discriminative model D. In particular, we build the generator G as an agent of reinforcement learning, which takes the raw text as input and predicts the abstractive summarization.
Ranked #5 on Text Summarization on CNN / Daily Mail (Anonymized)
Abstractive Text Summarization Generative Adversarial Network +2
no code implementations • HLT 2016 • Linqing Liu, Yao Lu, Ye Luo, Renxian Zhang, Laurent Itti, Jianwei Lu
Spammer detection on social network is a challenging problem.