Willem Zuidema


2020

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DoLFIn: Distributions over Latent Features for Interpretability
Phong Le | Willem Zuidema
Proceedings of the 28th International Conference on Computational Linguistics

Interpreting the inner workings of neural models is a key step in ensuring the robustness and trustworthiness of the models, but work on neural network interpretability typically faces a trade-off: either the models are too constrained to be very useful, or the solutions found by the models are too complex to interpret. We propose a novel strategy for achieving interpretability that – in our experiments – avoids this trade-off. Our approach builds on the success of using probability as the central quantity, such as for instance within the attention mechanism. In our architecture, DoLFIn (Distributions over Latent Features for Interpretability), we do no determine beforehand what each feature represents, and features go altogether into an unordered set. Each feature has an associated probability ranging from 0 to 1, weighing its importance for further processing. We show that, unlike attention and saliency map approaches, this set-up makes it straight-forward to compute the probability with which an input component supports the decision the neural model makes. To demonstrate the usefulness of the approach, we apply DoLFIn to text classification, and show that DoLFIn not only provides interpretable solutions, but even slightly outperforms the classical CNN and BiLSTM text classifiers on the SST2 and AG-news datasets.

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Quantifying Attention Flow in Transformers
Samira Abnar | Willem Zuidema
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

In the Transformer model, “self-attention” combines information from attended embeddings into the representation of the focal embedding in the next layer. Thus, across layers of the Transformer, information originating from different tokens gets increasingly mixed. This makes attention weights unreliable as explanations probes. In this paper, we consider the problem of quantifying this flow of information through self-attention. We propose two methods for approximating the attention to input tokens given attention weights, attention rollout and attention flow, as post hoc methods when we use attention weights as the relative relevance of the input tokens. We show that these methods give complementary views on the flow of information, and compared to raw attention, both yield higher correlations with importance scores of input tokens obtained using an ablation method and input gradients.

2019

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Analysing Neural Language Models: Contextual Decomposition Reveals Default Reasoning in Number and Gender Assignment
Jaap Jumelet | Willem Zuidema | Dieuwke Hupkes
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

Extensive research has recently shown that recurrent neural language models are able to process a wide range of grammatical phenomena. How these models are able to perform these remarkable feats so well, however, is still an open question. To gain more insight into what information LSTMs base their decisions on, we propose a generalisation of Contextual Decomposition (GCD). In particular, this setup enables us to accurately distil which part of a prediction stems from semantic heuristics, which part truly emanates from syntactic cues and which part arise from the model biases themselves instead. We investigate this technique on tasks pertaining to syntactic agreement and co-reference resolution and discover that the model strongly relies on a default reasoning effect to perform these tasks.

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Blackbox Meets Blackbox: Representational Similarity & Stability Analysis of Neural Language Models and Brains
Samira Abnar | Lisa Beinborn | Rochelle Choenni | Willem Zuidema
Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

In this paper, we define and apply representational stability analysis (ReStA), an intuitive way of analyzing neural language models. ReStA is a variant of the popular representational similarity analysis (RSA) in cognitive neuroscience. While RSA can be used to compare representations in models, model components, and human brains, ReStA compares instances of the same model, while systematically varying single model parameter. Using ReStA, we study four recent and successful neural language models, and evaluate how sensitive their internal representations are to the amount of prior context. Using RSA, we perform a systematic study of how similar the representational spaces in the first and second (or higher) layers of these models are to each other and to patterns of activation in the human brain. Our results reveal surprisingly strong differences between language models, and give insights into where the deep linguistic processing, that integrates information over multiple sentences, is happening in these models. The combination of ReStA and RSA on models and brains allows us to start addressing the important question of what kind of linguistic processes we can hope to observe in fMRI brain imaging data. In particular, our results suggest that the data on story reading from Wehbe et al./ (2014) contains a signal of shallow linguistic processing, but show no evidence on the more interesting deep linguistic processing.

2018

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Experiential, Distributional and Dependency-based Word Embeddings have Complementary Roles in Decoding Brain Activity
Samira Abnar | Rasyan Ahmed | Max Mijnheer | Willem Zuidema
Proceedings of the 8th Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2018)

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Under the Hood: Using Diagnostic Classifiers to Investigate and Improve how Language Models Track Agreement Information
Mario Giulianelli | Jack Harding | Florian Mohnert | Dieuwke Hupkes | Willem Zuidema
Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

How do neural language models keep track of number agreement between subject and verb? We show that ‘diagnostic classifiers’, trained to predict number from the internal states of a language model, provide a detailed understanding of how, when, and where this information is represented. Moreover, they give us insight into when and where number information is corrupted in cases where the language model ends up making agreement errors. To demonstrate the causal role played by the representations we find, we then use agreement information to influence the course of the LSTM during the processing of difficult sentences. Results from such an intervention reveal a large increase in the language model’s accuracy. Together, these results show that diagnostic classifiers give us an unrivalled detailed look into the representation of linguistic information in neural models, and demonstrate that this knowledge can be used to improve their performance.

2016

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Quantifying the Vanishing Gradient and Long Distance Dependency Problem in Recursive Neural Networks and Recursive LSTMs
Phong Le | Willem Zuidema
Proceedings of the 1st Workshop on Representation Learning for NLP

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Generalization in Artificial Language Learning: Modelling the Propensity to Generalize
Raquel G. Alhama | Willem Zuidema
Proceedings of the 7th Workshop on Cognitive Aspects of Computational Language Learning

2015

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The Forest Convolutional Network: Compositional Distributional Semantics with a Neural Chart and without Binarization
Phong Le | Willem Zuidema
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Unsupervised Dependency Parsing: Let’s Use Supervised Parsers
Phong Le | Willem Zuidema
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Compositional Distributional Semantics with Long Short Term Memory
Phong Le | Willem Zuidema
Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

2014

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The Inside-Outside Recursive Neural Network model for Dependency Parsing
Phong Le | Willem Zuidema
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2013

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Learning from errors: Using vector-based compositional semantics for parse reranking
Phong Le | Willem Zuidema | Remko Scha
Proceedings of the Workshop on Continuous Vector Space Models and their Compositionality

2012

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Learning Compositional Semantics for Open Domain Semantic Parsing
Phong Le | Willem Zuidema
Proceedings of COLING 2012

2011

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Accurate Parsing with Compact Tree-Substitution Grammars: Double-DOP
Federico Sangati | Willem Zuidema
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

2010

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Efficiently Extract Rrecurring Tree Fragments from Large Treebanks
Federico Sangati | Willem Zuidema | Rens Bod
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

In this paper we describe FragmentSeeker, a tool which is capable to identify all those tree constructions which are recurring multiple times in a large Phrase Structure treebank. The tool is based on an efficient kernel-based dynamic algorithm, which compares every pair of trees of a given treebank and computes the list of fragments which they both share. We describe two different notions of fragments we will use, i.e. standard and partial fragments, and provide the implementation details on how to extract them from a syntactically annotated corpus. We have tested our system on the Penn Wall Street Journal treebank for which we present quantitative and qualitative analysis on the obtained recurring structures, as well as provide empirical time performance. Finally we propose possible ways our tool could contribute to different research fields related to corpus analysis and processing, such as parsing, corpus statistics, annotation guidance, and automatic detection of argument structure.

2009

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Unsupervised Methods for Head Assignments
Federico Sangati | Willem Zuidema
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)

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A generative re-ranking model for dependency parsing
Federico Sangati | Willem Zuidema | Rens Bod
Proceedings of the 11th International Conference on Parsing Technologies (IWPT’09)

2007

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Parsimonious Data-Oriented Parsing
Willem Zuidema
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

2006

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Theoretical Evaluation of Estimation Methods for Data-Oriented Parsing
Willem Zuidema
Demonstrations

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What are the Productive Units of Natural Language Grammar? A DOP Approach to the Automatic Identification of Constructions.
Willem Zuidema
Proceedings of the Tenth Conference on Computational Natural Language Learning (CoNLL-X)