206 citations · 347 across the 17 of their papers we have counts for
5 papers · 1 filter
Learning Symbolic Rules over Abstract Meaning Representations for Textual Reinforcement Learning
Subhajit Chaudhury, Sarathkrishna Swaminathan, Daiki Kimura +8
Text-based reinforcement learning agents have predominantly been neural network-based models with embeddings-based representation, learning uninterpretable policies that often do n…
Are Human Explanations Always Helpful? Towards Objective Evaluation of Human Natural Language Explanations
Bingsheng Yao, Prithviraj Sen, Lucian Popa +2
Human-annotated labels and explanations are critical for training explainable NLP models. However, unlike human-annotated labels whose quality is easier to calibrate (e.g., with a…
LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking
Hang Jiang, Sairam Gurajada, Qiuhao Lu +5
Entity linking (EL), the task of disambiguating mentions in text by linking them to entities in a knowledge graph, is crucial for text understanding, question answering or conversa…
A Survey of the State of Explainable AI for Natural Language Processing
Marina Danilevsky, Kun Qian, Ranit Aharonov +3
Recent years have seen important advances in the quality of state-of-the-art models, but this has come at the expense of models becoming less interpretable. This survey presents an…
HEIDL: Learning Linguistic Expressions with Deep Learning and Human-in-the-Loop
Yiwei Yang, Eser Kandogan, Yunyao Li +2
While the role of humans is increasingly recognized in machine learning community, representation of and interaction with models in current human-in-the-loop machine learning (HITL…