no code implementations • EMNLP 2020 • Nitish Shirish Keskar, Bryan McCann, Caiming Xiong, Richard Socher
Pre-training in natural language processing makes it easier for an adversary with only query access to a victim model to reconstruct a local copy of the victim by training with gibberish input data paired with the victim{'}s labels for that data.
1 code implementation • EACL 2021 • Tianxing He, Bryan McCann, Caiming Xiong, Ehsan Hosseini-Asl
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.
1 code implementation • 8 Dec 2020 • Junxian He, Wojciech Kryściński, Bryan McCann, Nazneen Rajani, Caiming Xiong
Our approach enables users to control multiple aspects of generated summaries by interacting with the summarization system through textual input in the form of a set of keywords or descriptive prompts.
no code implementations • 6 Nov 2020 • Hiroaki Hayashi, Wojciech Kryściński, Bryan McCann, Nazneen Rajani, Caiming Xiong
To overcome this problem, we introduce a new task of disentangled paper summarization, which seeks to generate separate summaries for the paper contributions and the context of the work, making it easier to identify the key findings shared in articles.
no code implementations • Findings (EMNLP) 2021 • Gustavo Aguilar, Bryan McCann, Tong Niu, Nazneen Rajani, Nitish Keskar, Thamar Solorio
To alleviate these challenges, we propose a character-based subword module (char2subword) that learns the subword embedding table in pre-trained models like BERT.
3 code implementations • Findings (EMNLP) 2021 • Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann, Nitish Shirish Keskar, Shafiq Joty, Richard Socher, Nazneen Fatema Rajani
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 the distribution they generate.
5 code implementations • 24 Jul 2020 • Alexander R. Fabbri, Wojciech Kryściński, Bryan McCann, Caiming Xiong, Richard Socher, Dragomir Radev
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.
1 code implementation • ACL 2020 • Tianlu Wang, Xi Victoria Lin, Nazneen Fatema Rajani, Bryan McCann, Vicente Ordonez, Caiming Xiong
Word embeddings derived from human-generated corpora inherit strong gender bias which can be further amplified by downstream models.
1 code implementation • NeurIPS 2020 • Ehsan Hosseini-Asl, Bryan McCann, Chien-Sheng Wu, Semih Yavuz, Richard Socher
Task-oriented dialogue is often decomposed into three tasks: understanding user input, deciding actions, and generating a response.
Ranked #2 on Response Generation on MMConv
2 code implementations • 8 Mar 2020 • Ali Madani, Bryan McCann, Nikhil Naik, Nitish Shirish Keskar, Namrata Anand, Raphael R. Eguchi, Po-Ssu Huang, Richard Socher
Generative modeling for protein engineering is key to solving fundamental problems in synthetic biology, medicine, and material science.
1 code implementation • WS 2019 • Jasdeep Singh, Bryan McCann, Richard Socher, Caiming Xiong
Multilingual transfer learning can benefit both high- and low-resource languages, but the source of these improvements is not well understood.
4 code implementations • EMNLP 2020 • Wojciech Kryściński, Bryan McCann, Caiming Xiong, Richard Socher
Currently used metrics for assessing summarization algorithms do not account for whether summaries are factually consistent with source documents.
7 code implementations • Preprint 2019 • Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, Caiming Xiong, Richard Socher
Large-scale language models show promising text generation capabilities, but users cannot easily control particular aspects of the generated text.
no code implementations • IJCNLP 2019 • Wojciech Kryściński, Nitish Shirish Keskar, Bryan McCann, Caiming Xiong, Richard Socher
Text summarization aims at compressing long documents into a shorter form that conveys the most important parts of the original document.
1 code implementation • ACL 2019 • Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, Richard Socher
Deep learning models perform poorly on tasks that require commonsense reasoning, which often necessitates some form of world-knowledge or reasoning over information not immediately present in the input.
Ranked #22 on Common Sense Reasoning on CommonsenseQA
no code implementations • ICLR 2020 • Jasdeep Singh, Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, Richard Socher
XLDA is in contrast to, and performs markedly better than, a more naive approach that aggregates examples in various languages in a way that each example is solely in one language.
Cross-Lingual Natural Language Inference Data Augmentation +3
no code implementations • 19 Apr 2019 • Nitish Shirish Keskar, Bryan McCann, Caiming Xiong, Richard Socher
Even as pre-trained language encoders such as BERT are shared across many tasks, the output layers of question answering, text classification, and regression models are significantly different.
5 code implementations • ICLR 2019 • Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, Richard Socher
Though designed for decaNLP, MQAN also achieves state of the art results on the WikiSQL semantic parsing task in the single-task setting.
no code implementations • 3 Aug 2017 • Stephen Merity, Bryan McCann, Richard Socher
Both of these techniques require minimal modification to existing RNN architectures and result in performance improvements comparable or superior to more complicated regularization techniques or custom cell architectures.
5 code implementations • NeurIPS 2017 • Bryan McCann, James Bradbury, Caiming Xiong, Richard Socher
For fine-grained sentiment analysis and entailment, CoVe improves performance of our baseline models to the state of the art.
Ranked #9 on Text Classification on TREC-6