68 citations · 218 across the 63 of their papers we have counts for
10 papers · 1 filter
Quantifying the Task-Specific Information in Text-Based Classifications
Zining Zhu, Aparna Balagopalan, Marzyeh Ghassemi +1
Recently, neural natural language models have attained state-of-the-art performance on a wide variety of tasks, but the high performance can result from superficial, surface-level…
Language Modelling via Learning to Rank
Arvid Frydenlund, Gagandeep Singh, Frank Rudzicz
We consider language modelling (LM) as a multi-label structured prediction task by re-framing training from solely predicting a single ground-truth word to ranking a set of words w…
An unsupervised framework for tracing textual sources of moral change
Aida Ramezani, Zining Zhu, Frank Rudzicz +1
Morality plays an important role in social well-being, but people's moral perception is not stable and changes over time. Recent advances in natural language processing have shown…
What do writing features tell us about AI papers?
Zining Zhu, Bai Li, Yang Xu +1
As the numbers of submissions to conferences grow quickly, the task of assessing the quality of academic papers automatically, convincingly, and with high accuracy attracts increas…
How is BERT surprised? Layerwise detection of linguistic anomalies
Bai Li, Zining Zhu, Guillaume Thomas +2
Transformer language models have shown remarkable ability in detecting when a word is anomalous in context, but likelihood scores offer no information about the cause of the anomal…
TorontoCL at CMCL 2021 Shared Task: RoBERTa with Multi-Stage Fine-Tuning for Eye-Tracking Prediction
Bai Li, Frank Rudzicz
Eye movement data during reading is a useful source of information for understanding language comprehension processes. In this paper, we describe our submission to the CMCL 2021 sh…
On the Use of Linguistic Features for the Evaluation of Generative Dialogue Systems
Ian Berlot-Attwell, Frank Rudzicz
Automatically evaluating text-based, non-task-oriented dialogue systems (i.e., `chatbots') remains an open problem. Previous approaches have suffered challenges ranging from poor c…
Challenges for Reinforcement Learning in Healthcare
Elsa Riachi, Muhammad Mamdani, Michael Fralick +1
Many healthcare decisions involve navigating through a multitude of treatment options in a sequential and iterative manner to find an optimal treatment pathway with the goal of an…
Speaker attribution with voice profiles by graph-based semi-supervised learning
Jixuan Wang, Xiong Xiao, Jian Wu +3
Speaker attribution is required in many real-world applications, such as meeting transcription, where speaker identity is assigned to each utterance according to speaker voice prof…
BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data
Demetres Kostas, Stephane Aroca-Ouellette, Frank Rudzicz
Deep neural networks (DNNs) used for brain-computer-interface (BCI) classification are commonly expected to learn general features when trained across a variety of contexts, such t…