Search Results for author:
Found 14 papers, 3 papers with code
An Embarrassingly Simple Method to Mitigate Undesirable Properties of Pretrained Language Model Tokenizers
We introduce FLOTA (Few Longest Token Approximation), a simple yet effective method to improve the tokenization of pretrained language models (PLMs).
An Information-Theoretic Approach and Dataset for Probing Gender Stereotypes in Multilingual Masked Language Models
Bias research in NLP is a rapidly growing and developing field.
Problem Solving Through Human-AI Preference-Based Cooperation
While there is a widespread belief that artificial general intelligence (AGI) -- or even superhuman AI -- is imminent, complex problems in expert domains are far from being solved.
Language Models with Rationality
To address this, our goals are to make model beliefs and their inferential relationships explicit, and to resolve inconsistencies that may exist, so that answers are supported by interpretable chains of reasoning drawn from a consistent network of beliefs.
Listening to Affected Communities to Define Extreme Speech: Dataset and Experiments
Building on current work on multilingual hate speech (e. g., Ousidhoum et al. (2019)) and hate speech reduction (e. g., Sap et al. (2020)), we present XTREMESPEECH, a new hate speech dataset containing 20, 297 social media passages from Brazil, Germany, India and Kenya.
Differentiable Multi-Agent Actor-Critic for Multi-Step Radiology Report Summarization
A cascade of tasks are required to automatically generate an abstractive summary of the typical information-rich radiology report.
Few-Shot Learning of an Interleaved Text Summarization Model by Pretraining with Synthetic Data
Interleaved texts, where posts belonging to different threads occur in a sequence, commonly occur in online chat posts, so that it can be time-consuming to quickly obtain an overview of the discussions.
Nonsymbolic Text Representation
We introduce the first generic text representation model that is completely nonsymbolic, i. e., it does not require the availability of a segmentation or tokenization method that attempts to identify words or other symbolic units in text.
Two SVDs produce more focal deep learning representations
A key characteristic of work on deep learning and neural networks in general is that it relies on representations of the input that support generalization, robust inference, domain adaptation and other desirable functionalities.
Cutting Recursive Autoencoder Trees
This makes the analysis of learned structures particularly difficult.
