4 citations · 9 across the 4 of their papers we have counts for
6 papers · 1 filter
Blindness to Modality Helps Entailment Graph Mining
Liane Guillou, Sander Bijl de Vroe, Mark Johnson +1
Understanding linguistic modality is widely seen as important for downstream tasks such as Question Answering and Knowledge Graph Population. Entailment Graph learning might also b…
Incorporating Temporal Information in Entailment Graph Mining
Liane Guillou, Sander Bijl de Vroe, Mohammad Javad Hosseini +2
We present a novel method for injecting temporality into entailment graphs to address the problem of spurious entailments, which may arise from similar but temporally distinct even…
Neural Rule-Execution Tracking Machine For Transformer-Based Text Generation
Yufei Wang, Can Xu, Huang Hu +5
Sequence-to-Sequence (S2S) neural text generation models, especially the pre-trained ones (e.g., BART and T5), have exhibited compelling performance on various natural language gen…
Detecting and Exorcising Statistical Demons from Language Models with Anti-Models of Negative Data
Michael L. Wick, Kate Silverstein, Jean-Baptiste Tristan +2
It's been said that "Language Models are Unsupervised Multitask Learners." Indeed, self-supervised language models trained on "positive" examples of English text generalize in desi…
Improving Disfluency Detection by Self-Training a Self-Attentive Model
Paria Jamshid Lou, Mark Johnson
Self-attentive neural syntactic parsers using contextualized word embeddings (e.g. ELMo or BERT) currently produce state-of-the-art results in joint parsing and disfluency detectio…
How to best use Syntax in Semantic Role Labelling
Yufei Wang, Mark Johnson, Stephen Wan +2
There are many different ways in which external information might be used in an NLP task. This paper investigates how external syntactic information can be used most effectively in…