61 citations · 161 across the 5 of their papers we have counts for
14 papers · 1 filter
Joint Energy-based Model Training for Better Calibrated Natural Language Understanding Models
Tianxing He, Bryan McCann, Caiming Xiong +1
In this work, we explore joint energy-based model (EBM) training during the finetuning of pretrained text encoders (e.g., Roberta) for natural language understanding (NLU) tasks. O…
CTRLsum: Towards Generic Controllable Text Summarization
Junxian He, Wojciech Kryściński, Bryan McCann +2
Current summarization systems yield generic summaries that are disconnected from users' preferences and expectations. To address this limitation, we present CTRLsum, a novel framew…
What's New? Summarizing Contributions in Scientific Literature
Hiroaki Hayashi, Wojciech Kryściński, Bryan McCann +2
With thousands of academic articles shared on a daily basis, it has become increasingly difficult to keep up with the latest scientific findings. To overcome this problem, we intro…
GeDi: Generative Discriminator Guided Sequence Generation
Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann +4
While large-scale language models (LMs) are able to imitate the distribution of natural language well enough to generate realistic text, it is difficult to control which regions of…
Char2Subword: Extending the Subword Embedding Space Using Robust Character Compositionality
Gustavo Aguilar, Bryan McCann, Tong Niu +3
Byte-pair encoding (BPE) is a ubiquitous algorithm in the subword tokenization process of language models as it provides multiple benefits. However, this process is solely based on…
SummEval: Re-evaluating Summarization Evaluation
Alexander R. Fabbri, Wojciech Kryściński, Bryan McCann +3
The scarcity of comprehensive up-to-date studies on evaluation metrics for text summarization and the lack of consensus regarding evaluation protocols continue to inhibit progress.…