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Found 6 papers, 1 papers with code
Learning Modulo Theories
Recent techniques that integrate \emph{solver layers} into Deep Neural Networks (DNNs) have shown promise in bridging a long-standing gap between inductive learning and symbolic reasoning techniques.
Order-sensitive Shapley Values for Evaluating Conceptual Soundness of NLP Models
Previous works show that deep NLP models are not always conceptually sound: they do not always learn the correct linguistic concepts.
Influence Patterns for Explaining Information Flow in BERT
While attention is all you need may be proving true, we do not know why: attention-based transformer models such as BERT are superior but how information flows from input tokens to output predictions are unclear.
ABSTRACTING INFLUENCE PATHS FOR EXPLAINING (CONTEXTUALIZATION OF) BERT MODELS
While “attention is all you need” may be proving true, we do not yet know why: attention-based transformer models such as BERT are superior but how they contextualize information even for simple grammatical rules such as subject-verb number agreement(SVA) is uncertain.
Influence Paths for Characterizing Subject-Verb Number Agreement in LSTM Language Models
LSTM-based recurrent neural networks are the state-of-the-art for many natural language processing (NLP) tasks.
Gender Bias in Neural Natural Language Processing
We define a general benchmark to quantify gender bias in a variety of neural NLP tasks.
