Yangfeng Ji

deltaBLEU: A Discriminative Metric for Generation Tasks with Intrinsically Diverse Targets

2015 · arXiv (Cornell University)

We introduce Discriminative BLEU (deltaBLEU), a novel metric for intrinsic evaluation of generated text in tasks that admit a diverse range of possible outputs. Reference strings are scored for quality by human raters on a …

Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group Masks

2021 · arXiv (Cornell University)

Explaining neural network models is important for increasing their trustworthiness in real-world applications. Most existing methods generate post-hoc explanations for neural network models by identifying individual feature attributions or detecting interactions between adjacent features. However, …

Pathologies of Pre-trained Language Models in Few-shot Fine-tuning

2022

Although adapting pre-trained language models with few examples has shown promising performance on text classification, there is a lack of understanding of where the performance gain comes from. In this work, we propose to answer …

SelectFormer: Private and Practical Data Selection for Transformers

2023 · arXiv (Cornell University)

Critical to a free data market is $\textit{private data selection}$, i.e. the model owner selects and then appraises training data from the data owner before both parties commit to a transaction. To keep the data …

Monte Carlo Sampling for Analyzing In-Context Examples

2025 · arXiv (Cornell University)

Prior works have shown that in-context learning is brittle to presentation factors such as the order, number, and choice of selected examples. However, ablation-based guidance on selecting the number of examples may ignore the interplay …

A Neural Network Approach to Context-Sensitive Generation of Conversational Responses

2015

Alessandro Sordoni, Michel Galley, Michael Auli, Chris Brockett, Yangfeng Ji, Margaret Mitchell, Jian-Yun Nie, Jianfeng Gao, Bill Dolan. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human …

Neural Text Generation in Stories Using Entity Representations as Context

2018

Elizabeth Clark, Yangfeng Ji, Noah A. Smith. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.

Learning Variational Word Masks to Improve the Interpretability of Neural Text Classifiers

2020

To build an interpretable neural text classifier, most of the prior work has focused on designing inherently interpretable models or finding faithful explanations. A new line of work on improving model interpretability has just started, …

The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics

2021

Sebastian Gehrmann, Tosin Adewumi, Karmanya Aggarwal, Pawan Sasanka Ammanamanchi, Anuoluwapo Aremu, Antoine Bosselut, Khyathi Raghavi Chandu, Miruna-Adriana Clinciu, Dipanjan Das, Kaustubh Dhole, Wanyu Du, Esin Durmus, Ondřej Dušek, Chris Chinenye Emezue, Varun Gangal, Cristina Garbacea, …