Natural Language Inference (NLI) is a key, complex task where machine learning (ML) is playing an important role. However, ML has progressively obfuscated the role of linguistically-motivated inference rules, which should be the core of NLI systems. In this paper, we introduce distributed inference rules as a novel way to encode linguistically-motivated inference rules in learning interpretable NLI classifiers. We propose two encoders: the Distributed Partial Tree Encoder and the Distributed Smoothed Partial Tree Encoder. These encoders allow modeling syntactic and syntactic-semantic inference rules as distributed representations ready to be used in ML models over large datasets. Although far from the state-of-the-art of end-to-end deep learning systems on large datasets, our shallow networks positively exploit inference rules for NLI, improving over baseline systems. This is a first positive step towards interpretable and explainable end-to-end deep learning systems.

Zanzotto, F.m., & Ferrone, L. (2017). Can we explain natural language inference decisions taken with neural networks? Inference rules in distributed representations. In Proceedings of the International Joint Conference on Neural Networks (pp.3680-3687). Institute of Electrical and Electronics Engineers Inc. [10.1109/IJCNN.2017.7966319].

Can we explain natural language inference decisions taken with neural networks? Inference rules in distributed representations

Zanzotto, Fabio Massimo;Ferrone, Lorenzo
2017

Abstract

Natural Language Inference (NLI) is a key, complex task where machine learning (ML) is playing an important role. However, ML has progressively obfuscated the role of linguistically-motivated inference rules, which should be the core of NLI systems. In this paper, we introduce distributed inference rules as a novel way to encode linguistically-motivated inference rules in learning interpretable NLI classifiers. We propose two encoders: the Distributed Partial Tree Encoder and the Distributed Smoothed Partial Tree Encoder. These encoders allow modeling syntactic and syntactic-semantic inference rules as distributed representations ready to be used in ML models over large datasets. Although far from the state-of-the-art of end-to-end deep learning systems on large datasets, our shallow networks positively exploit inference rules for NLI, improving over baseline systems. This is a first positive step towards interpretable and explainable end-to-end deep learning systems.
2017 International Joint Conference on Neural Networks, IJCNN 2017
usa
2017
Brain-Mind Institute (BMI)
Rilevanza internazionale
contributo
Settore INF/01 - Informatica
Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni
English
Software; Artificial Intelligence
Intervento a convegno
Zanzotto, F.m., & Ferrone, L. (2017). Can we explain natural language inference decisions taken with neural networks? Inference rules in distributed representations. In Proceedings of the International Joint Conference on Neural Networks (pp.3680-3687). Institute of Electrical and Electronics Engineers Inc. [10.1109/IJCNN.2017.7966319].
Zanzotto, Fm; Ferrone, L
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2108/190405
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