Instance-Based Neural Dependency Parsing
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Abstract (arXiv)
Interpretable rationales for model predictions are crucial in practical applications. We develop neural models that possess an interpretable inference process for dependency parsing. Our models adopt instance-based inference, where dependency edges are extracted and labeled by comparing them to edges in a training set. The training edges are explicitly used for the predictions; thus, it is easy to grasp the contribution of each edge to the predictions. Our experiments show that our instance-based models achieve competitive accuracy with standard neural models and have the reasonable plausibility of instance-based explanations.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 31 of its 76 references that are in the map. First posted on arXiv 28 Sep 2021.
Builds on
31 of 76 references.
References · 31
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
- 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
- 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
- 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
- 2018CosFace: Large Margin Cosine Loss for Deep Face Recognitioncited by 968
- 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 929
- 2019Generalization through Memorization: Nearest Neighbor Language Modelscited by 788
- 2017SphereFace: Deep Hypersphere Embedding for Face Recognitioncited by 733
- 2014Deep metric learning using Triplet networkcited by 421
- 2020Circle Loss: A Unified Perspective of Pair Similarity Optimizationcited by 402
19 more
- 2019Multi-Similarity Loss with General Pair Weighting for Deep Metric Learningcited by 368
- 2016Deep Biaffine Attention for Neural Dependency Parsingcited by 334
- 2016Rationalizing Neural Predictionscited by 281
- 2018Learning Word Vectors for 157 Languagescited by 279
- 2020Nearest Neighbor Machine Translationcited by 209
- 2018Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learningcited by 203
- 2014Learning Fine-grained Image Similarity with Deep Rankingcited by 189
- 2015Holographic Embeddings of Knowledge Graphscited by 181
- 2017L2-constrained Softmax Loss for Discriminative Face Verificationcited by 96
- 2018Model Agnostic Supervised Local Explanationscited by 83
- 2018Semi-Supervised Sequence Modeling with Cross-View Trainingcited by 75
- 2016A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Taskscited by 62
- 2013A Survey on Metric Learning for Feature Vectors and Structured Datacited by 57
- 2020Efficient Second-Order TreeCRF for Neural Dependency Parsingcited by 48
- 2019Label-Agnostic Sequence Labeling by Copying Nearest Neighborscited by 27
- 2020Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognitioncited by 23
- 2016Dependency Parsing as Head Selectioncited by 13
- 2019Deep Contextualized Word Embeddings in Transition-Based and Graph-Based Dependency Parsing -- A Tale of Two Parsers Revisitedcited by 13
- 2018An Investigation of the Interactions Between Pre-Trained Word Embeddings, Character Models and POS Tags in Dependency Parsingcited by 10
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