DS-2020-06: Hupkes, Dieuwke (2020) Hierarchy and interpretability in neural models of language processing. Doctoral thesis, University of Amsterdam.
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Abstract
Artificial neural networks have become remarkably successful on many natural language processing tasks. In this dissertation, I explore if these successes make them useful as explanatory models of human language processing, focusing in particular on hierarchical compositionality and recurrent neural networks (RNNs). I consider two questions:
- Are RNNs in fact capable of processing hierarchical compositional structures?
- How can we obtain insight in how they do so?
This dissertation is divided into three parts. In part one, I consider artificial languages, which provide a clean setup in which processing of structure can be studied in isolation. In this part, I also introduce diagnostic classification -- an interpretability technique that plays an important role in this dissertation -- and reflect upon what it means for a model to be able to process hierarchical compositionality.
In part two, I consider language models, trained on naturalistic data. Such models have been shown to capture syntax-sensitive long-distance subject-verb relationships. I investigate how they do so. I present detailed analyses of their inner dynamics, using diagnostic classification, neuron ablation and generalised contextual decomposition.
Lastly, in part three, I consider if a model's solution can be changed through an adapted learning signal.
In summary, in this dissertation I present many different analyses concerning the abilities of RNNs to process hierarchical structure, as well as several techniques to understand these blackbox models. The results sketch a positive picture of the usefulness of such models as explanatory models of processing languages with hierarchical compositional semantics.
Item Type: | Thesis (Doctoral) |
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Report Nr: | DS-2020-06 |
Series Name: | ILLC Dissertation (DS) Series |
Year: | 2020 |
Subjects: | Language |
Depositing User: | Dr Marco Vervoort |
Date Deposited: | 14 Jun 2022 15:17 |
Last Modified: | 14 Jun 2022 15:17 |
URI: | https://eprints.illc.uva.nl/id/eprint/2175 |
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