Search Results for author: Ping Nie

Found 7 papers, 2 papers with code

Beyond Prompting: Making Pre-trained Language Models Better Zero-shot Learners by Clustering Representations

1 code implementation29 Oct 2022 Yu Fei, Ping Nie, Zhao Meng, Roger Wattenhofer, Mrinmaya Sachan

We further explore the applicability of our clustering approach by evaluating it on 14 datasets with more diverse topics, text lengths, and numbers of classes.

Clustering Sentence +7

Anticipating the Unseen Discrepancy for Vision and Language Navigation

no code implementations10 Sep 2022 Yujie Lu, Huiliang Zhang, Ping Nie, Weixi Feng, Wenda Xu, Xin Eric Wang, William Yang Wang

In this paper, we propose an Unseen Discrepancy Anticipating Vision and Language Navigation (DAVIS) that learns to generalize to unseen environments via encouraging test-time visual consistency.

Data Augmentation Decision Making +3

MIC: Model-agnostic Integrated Cross-channel Recommenders

no code implementations22 Oct 2021 Yujie Lu, Ping Nie, Shengyu Zhang, Ming Zhao, Ruobing Xie, William Yang Wang, Yi Ren

However, existing work are primarily built upon pre-defined retrieval channels, including User-CF (U2U), Item-CF (I2I), and Embedding-based Retrieval (U2I), thus access to the limited correlation between users and items which solely entail from partial information of latent interactions.

Recommendation Systems Retrieval +2

Answering Any-hop Open-domain Questions with Iterative Document Reranking

no code implementations16 Sep 2020 Ping Nie, Yuyu Zhang, Arun Ramamurthy, Le Song

Existing approaches for open-domain question answering (QA) are typically designed for questions that require either single-hop or multi-hop reasoning, which make strong assumptions of the complexity of questions to be answered.

Multi-hop Question Answering Natural Questions +2

A Pairwise Probe for Understanding BERT Fine-Tuning on Machine Reading Comprehension

no code implementations2 Jun 2020 Jie Cai, Zhengzhou Zhu, Ping Nie, Qian Liu

In this paper, inspired by the observation that most probing tasks involve identifying matched pairs of phrases (e. g. coreference requires matching an entity and a pronoun), we propose a pairwise probe to understand BERT fine-tuning on the machine reading comprehension (MRC) task.

Boundary Detection coreference-resolution +1

DC-BERT: Decoupling Question and Document for Efficient Contextual Encoding

no code implementations28 Feb 2020 Yuyu Zhang, Ping Nie, Xiubo Geng, Arun Ramamurthy, Le Song, Daxin Jiang

Recent studies on open-domain question answering have achieved prominent performance improvement using pre-trained language models such as BERT.

Natural Questions Open-Domain Question Answering +1

Glyce: Glyph-vectors for Chinese Character Representations

2 code implementations NeurIPS 2019 Yuxian Meng, Wei Wu, Fei Wang, Xiaoya Li, Ping Nie, Fan Yin, Muyu Li, Qinghong Han, Xiaofei Sun, Jiwei Li

However, due to the lack of rich pictographic evidence in glyphs and the weak generalization ability of standard computer vision models on character data, an effective way to utilize the glyph information remains to be found.

Chinese Dependency Parsing Chinese Named Entity Recognition +21

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