scispace - formally typeset
N

Nanyun Peng

Researcher at University of California, Los Angeles

Publications -  197
Citations -  5298

Nanyun Peng is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Computer science & Sentence. The author has an hindex of 28, co-authored 145 publications receiving 3029 citations. Previous affiliations of Nanyun Peng include Johns Hopkins University & Texas A&M University.

Papers
More filters
Proceedings Article

Style Transfer in Text: Exploration and Evaluation

Zhenxin Fu, +4 more
TL;DR: Two models are explored to learn style transfer with non-parallel data to learn separate content representations and style representations using adversarial networks, and a novel evaluation metrics which measure two aspects of style transfer: transfer strength and content preservation.
Journal ArticleDOI

Cross-Sentence N-ary Relation Extraction with Graph LSTMs

Nanyun Peng, +4 more
- 05 Apr 2017 - 
TL;DR: A general relation extraction framework based on graph long short-term memory networks (graph LSTMs) that can be easily extended to cross-sentence n-ary relation extraction is explored, demonstrating its effectiveness with both conventional supervised learning and distant supervision.
Journal Article

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Aarohi Srivastava, +439 more
- 09 Jun 2022 - 
TL;DR: Evaluation of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters finds that model performance and calibration both improve with scale, but are poor in absolute terms.
Proceedings ArticleDOI

The Woman Worked as a Babysitter: On Biases in Language Generation

Emily Sheng, +3 more
TL;DR: The notion of the regard towards a demographic is introduced, the varying levels of regard towards different demographics are used as a defining metric for bias in NLG, and the extent to which sentiment scores are a relevant proxy metric for regard is analyzed.
Proceedings ArticleDOI

Named Entity Recognition for Chinese Social Media with Jointly Trained Embeddings

Nanyun Peng, +1 more
TL;DR: A new corpus of Weibo messages annotated for both name and nominal mentions is presented and a joint training objective for the embeddings that makes use of both (NER) labeled and unlabeled raw text is proposed.