2.1k citations · 4.1k across the 35 of their papers we have counts for
8 papers · 1 filter
Memory-Augmented Recurrent Neural Networks Can Learn Generalized Dyck Languages
Mirac Suzgun, Sebastian Gehrmann, Yonatan Belinkov +1
We introduce three memory-augmented Recurrent Neural Networks (MARNNs) and explore their capabilities on a series of simple language modeling tasks whose solutions require stack-ba…
exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformers Models
Benjamin Hoover, Hendrik Strobelt, Sebastian Gehrmann
Large language models can produce powerful contextual representations that lead to improvements across many NLP tasks. Since these models are typically guided by a sequence of lear…
Encoder-Agnostic Adaptation for Conditional Language Generation
Zachary M. Ziegler, Luke Melas-Kyriazi, Sebastian Gehrmann +1
Large pretrained language models have changed the way researchers approach discriminative natural language understanding tasks, leading to the dominance of approaches that adapt a…
Visual Interaction with Deep Learning Models through Collaborative Semantic Inference
Sebastian Gehrmann, Hendrik Strobelt, Robert Krüger +2
Automation of tasks can have critical consequences when humans lose agency over decision processes. Deep learning models are particularly susceptible since current black-box approa…
Evaluating an Automated Mediator for Joint Narratives in a Conflict Situation
Massimo Zancanaro, Oliviero Stock, Gianluca Schiavo +9
Joint narratives are often used in the context of reconciliation interventions for people in social conflict situations, which arise, for example, due to ethnic or religious differ…
GLTR: Statistical Detection and Visualization of Generated Text
Sebastian Gehrmann, Hendrik Strobelt, Alexander M. Rush
The rapid improvement of language models has raised the specter of abuse of text generation systems. This progress motivates the development of simple methods for detecting generat…
LSTM Networks Can Perform Dynamic Counting
Mirac Suzgun, Sebastian Gehrmann, Yonatan Belinkov +1
In this paper, we systematically assess the ability of standard recurrent networks to perform dynamic counting and to encode hierarchical representations. All the neural models in…
Improving Human Text Comprehension through Semi-Markov CRF-based Neural Section Title Generation
Sebastian Gehrmann, Steven Layne, Franck Dernoncourt
Titles of short sections within long documents support readers by guiding their focus towards relevant passages and by providing anchor-points that help to understand the progressi…