Search Results for author:
Found 7 papers, 2 papers with code
Transferring Representations of Logical Connectives
In modern natural language processing pipelines, it is common practice to “pretrain” a generative language model on a large corpus of text, and then to “finetune” the created representations by continuing to train them on a discriminative textual inference task.
Transformer Mechanisms Mimic Frontostriatal Gating Operations When Trained on Human Working Memory Tasks
Models based on the Transformer neural network architecture have seen success on a wide variety of tasks that appear to require complex "cognitive branching" -- or the ability to maintain pursuit of one goal while accomplishing others.
AND does not mean OR: Using Formal Languages to Study Language Models' Representations
A current open question in natural language processing is to what extent language models, which are trained with access only to the form of language, are able to capture the meaning of language.
Enhancing Review Comprehension with Domain-Specific Commonsense
In this paper, we introduce xSense, an effective system for review comprehension using domain-specific commonsense knowledge bases (xSense KBs).
Sampo: Unsupervised Knowledge Base Construction for Opinions and Implications
Since it can be expensive to obtain training data to learn to extract implications for each new domain of reviews, we propose an unsupervised KBC system, Sampo, Specifically, Sampo is tailored to build KBs for domains where many reviews on the same domain are available.
Optimal Transport-based Alignment of Learned Character Representations for String Similarity
We evaluate STANCE's ability to detect whether two strings can refer to the same entity--a task we term alias detection.
Seq2Seq Models with Dropout can Learn Generalizable Reduplication
Natural language reduplication can pose a challenge to neural models of language, and has been argued to require variables (Marcus et al., 1999).
