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Found 23 papers, 1 papers with code
BERTAC: Enhancing Transformer-based Language Models with Adversarially Pretrained Convolutional Neural Networks
We show that on the GLUE tasks, the combination of our pretrained CNN with ALBERT outperforms the original ALBERT and achieves a similar performance to that of SOTA.
Automatic Graph Partitioning for Very Large-scale Deep Learning
RaNNC also achieved better training throughputs than GPipe on both the enlarged BERT model pre-training (GPipe with hybrid parallelism) and the enlarged ResNet models (GPipe with model parallelism) in all of the settings we tried.
Understanding User Utterances in a Dialog System for Caregiving
A dialog system that can monitor the health status of seniors has a huge potential for solving the labor force shortage in the caregiving industry in aging societies.
Event Causality Recognition Exploiting Multiple Annotators' Judgments and Background Knowledge
Furthermore, we investigate the effect of supplying background knowledge to our classifiers.
Open-Domain Why-Question Answering with Adversarial Learning to Encode Answer Texts
In this paper, we propose a method for why-question answering (why-QA) that uses an adversarial learning framework.
DISAANA and D-SUMM: Large-scale Real Time NLP Systems for Analyzing Disaster Related Reports in Tweets
D-SUMM automatically summarizes a large number of disaster related reports concerning a specified area and helps rescue workers to understand disaster situations from a macro perspective.
WISDOM X, DISAANA and D-SUMM: Large-scale NLP Systems for Analyzing Textual Big Data
We demonstrate our large-scale NLP systems: WISDOM X, DISAANA, and D-SUMM.
