Yiding Hao


2020

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Metrical Grids and Generalized Tier Projection
Yiding Hao
Proceedings of the Society for Computation in Linguistics 2020

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Evaluating Attribution Methods using White-Box LSTMs
Yiding Hao
Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP

Interpretability methods for neural networks are difficult to evaluate because we do not understand the black-box models typically used to test them. This paper proposes a framework in which interpretability methods are evaluated using manually constructed networks, which we call white-box networks, whose behavior is understood a priori. We evaluate five methods for producing attribution heatmaps by applying them to white-box LSTM classifiers for tasks based on formal languages. Although our white-box classifiers solve their tasks perfectly and transparently, we find that all five attribution methods fail to produce the expected model explanations.

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Probabilistic Predictions of People Perusing: Evaluating Metrics of Language Model Performance for Psycholinguistic Modeling
Yiding Hao | Simon Mendelsohn | Rachel Sterneck | Randi Martinez | Robert Frank
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics

By positing a relationship between naturalistic reading times and information-theoretic surprisal, surprisal theory (Hale, 2001; Levy, 2008) provides a natural interface between language models and psycholinguistic models. This paper re-evaluates a claim due to Goodkind and Bicknell (2018) that a language model’s ability to model reading times is a linear function of its perplexity. By extending Goodkind and Bicknell’s analysis to modern neural architectures, we show that the proposed relation does not always hold for Long Short-Term Memory networks, Transformers, and pre-trained models. We introduce an alternate measure of language modeling performance called predictability norm correlation based on Cloze probabilities measured from human subjects. Our new metric yields a more robust relationship between language model quality and psycholinguistic modeling performance that allows for comparison between models with different training configurations.

2019

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Learnability and Overgeneration in Computational Syntax
Yiding Hao
Proceedings of the Society for Computation in Linguistics (SCiL) 2019

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Unbounded Stress in Subregular Phonology
Yiding Hao | Samuel Andersson
Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology

This paper situates culminative unbounded stress systems within the subregular hierarchy for functions. While Baek (2018) has argued that such systems can be uniformly understood as input tier-based strictly local constraints, we show here that default-to-opposite-side and default-to-same-side stress systems belong to distinct subregular classes when they are viewed as functions that assign primary stress to underlying forms. While the former system can be captured by input tier-based input strictly local functions, a subsequential function class that we define here, the latter system is not subsequential, though it is weakly deterministic according to McCollum et al.’s (2018) non-interaction criterion. Our results motivate the extension of recently proposed subregular language classes to subregular functions and argue in favor of McCollum et al’s definition of weak determinism over that of Heinz and Lai (2013).

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Action-Sensitive Phonological Dependencies
Yiding Hao | Dustin Bowers
Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology

This paper defines a subregular class of functions called the tier-based synchronized strictly local (TSSL) functions. These functions are similar to the the tier-based input-output strictly local (TIOSL) functions, except that the locality condition is enforced not on the input and output streams, but on the computation history of the minimal subsequential finite-state transducer. We show that TSSL functions naturally describe rhythmic syncope while TIOSL functions cannot, and we argue that TSSL functions provide a more restricted characterization of rhythmic syncope than existing treatments within Optimality Theory.

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Finding Hierarchical Structure in Neural Stacks Using Unsupervised Parsing
William Merrill | Lenny Khazan | Noah Amsel | Yiding Hao | Simon Mendelsohn | Robert Frank
Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

Neural network architectures have been augmented with differentiable stacks in order to introduce a bias toward learning hierarchy-sensitive regularities. It has, however, proven difficult to assess the degree to which such a bias is effective, as the operation of the differentiable stack is not always interpretable. In this paper, we attempt to detect the presence of latent representations of hierarchical structure through an exploration of the unsupervised learning of constituency structure. Using a technique due to Shen et al. (2018a,b), we extract syntactic trees from the pushing behavior of stack RNNs trained on language modeling and classification objectives. We find that our models produce parses that reflect natural language syntactic constituencies, demonstrating that stack RNNs do indeed infer linguistically relevant hierarchical structure.

2018

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Context-Free Transductions with Neural Stacks
Yiding Hao | William Merrill | Dana Angluin | Robert Frank | Noah Amsel | Andrew Benz | Simon Mendelsohn
Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

This paper analyzes the behavior of stack-augmented recurrent neural network (RNN) models. Due to the architectural similarity between stack RNNs and pushdown transducers, we train stack RNN models on a number of tasks, including string reversal, context-free language modelling, and cumulative XOR evaluation. Examining the behavior of our networks, we show that stack-augmented RNNs can discover intuitive stack-based strategies for solving our tasks. However, stack RNNs are more difficult to train than classical architectures such as LSTMs. Rather than employ stack-based strategies, more complex networks often find approximate solutions by using the stack as unstructured memory.

2017

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Harmonic Serialism and Finite-State Optimality Theory
Yiding Hao
Proceedings of the 13th International Conference on Finite State Methods and Natural Language Processing (FSMNLP 2017)