John Hale

Also published as: John T. Hale


2021

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Modeling Incremental Language Comprehension in the Brain with Combinatory Categorial Grammar
Miloš Stanojević | Shohini Bhattasali | Donald Dunagan | Luca Campanelli | Mark Steedman | Jonathan Brennan | John Hale
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics

Hierarchical sentence structure plays a role in word-by-word human sentence comprehension, but it remains unclear how best to characterize this structure and unknown how exactly it would be recognized in a step-by-step process model. With a view towards sharpening this picture, we model the time course of hemodynamic activity within the brain during an extended episode of naturalistic language comprehension using Combinatory Categorial Grammar (CCG). CCG has well-defined incremental parsing algorithms, surface compositional semantics, and can explain long-range dependencies as well as complicated cases of coordination. We find that CCG-derived predictors improve a regression model of fMRI time course in six language-relevant brain regions, over and above predictors derived from context-free phrase structure. Adding a special Revealing operator to CCG parsing, one designed to handle right-adjunction, improves the fit in three of these regions. This evidence for CCG from neuroimaging bolsters the more general case for mildly context-sensitive grammars in the cognitive science of language.

2020

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The Little Prince in 26 Languages: Towards a Multilingual Neuro-Cognitive Corpus
Sabrina Stehwien | Lena Henke | John Hale | Jonathan Brennan | Lars Meyer
Proceedings of the Second Workshop on Linguistic and Neurocognitive Resources

We present the Le Petit Prince Corpus (LPPC), a multi-lingual resource for research in (computational) psycho- and neurolinguistics. The corpus consists of the children’s story The Little Prince in 26 languages. The dataset is in the process of being built using state-of-the-art methods for speech and language processing and electroencephalography (EEG). The planned release of LPPC dataset will include raw text annotated with dependency graphs in the Universal Dependencies standard, a near-natural-sounding synthetic spoken subset as well as EEG recordings. We will use this corpus for conducting neurolinguistic studies that generalize across a wide range of languages, overcoming typological constraints to traditional approaches. The planned release of the LPPC combines linguistic and EEG data for many languages using fully automatic methods, and thus constitutes a readily extendable resource that supports cross-linguistic and cross-disciplinary research.

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Modeling conventionalization and predictability within MWEs at the brain level
Shohini Bhattasali | Murielle Fabre | Christophe Pallier | John Hale
Proceedings of the Society for Computation in Linguistics 2020

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The Alice Datasets: fMRI & EEG Observations of Natural Language Comprehension
Shohini Bhattasali | Jonathan Brennan | Wen-Ming Luh | Berta Franzluebbers | John Hale
Proceedings of the 12th Language Resources and Evaluation Conference

The Alice Datasets are a set of datasets based on magnetic resonance data and electrophysiological data, collected while participants heard a story in English. Along with the datasets and the text of the story, we provide a variety of different linguistic and computational measures ranging from prosodic predictors to predictors capturing hierarchical syntactic information. These ecologically valid datasets can be easily reused to replicate prior work and to test new hypotheses about natural language comprehension in the brain.

2019

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Text Genre and Training Data Size in Human-like Parsing
John Hale | Adhiguna Kuncoro | Keith Hall | Chris Dyer | Jonathan Brennan
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Domain-specific training typically makes NLP systems work better. We show that this extends to cognitive modeling as well by relating the states of a neural phrase-structure parser to electrophysiological measures from human participants. These measures were recorded as participants listened to a spoken recitation of the same literary text that was supplied as input to the neural parser. Given more training data, the system derives a better cognitive model — but only when the training examples come from the same textual genre. This finding is consistent with the idea that humans adapt syntactic expectations to particular genres during language comprehension (Kaan and Chun, 2018; Branigan and Pickering, 2017).

2018

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Differentiating Phrase Structure Parsing and Memory Retrieval in the Brain
Shohini Bhattasali | John Hale | Christophe Pallier | Jonathan Brennan | Wen-Ming Luh | R. Nathan Spreng
Proceedings of the Society for Computation in Linguistics (SCiL) 2018

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Modeling Brain Activity Associated with Pronoun Resolution in English and Chinese
Jixing Li | Murielle Fabre | Wen-Ming Luh | John Hale
Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference

Typological differences between English and Chinese suggest stronger reliance on salience of the antecedent during pronoun resolution in Chinese. We examined this hypothesis by correlating a difficulty measure of pronoun resolution derived by the activation-based ACT-R model with the brain activity of English and Chinese participants listening to a same audiobook during fMRI recording. The ACT-R model predicts higher overall difficulty for English speakers, which is supported at the brain level in left Broca’s area. More generally, it confirms that computational modeling approach is able to dissociate different dimensions that are involved in the complex process of pronoun resolution in the brain.

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The Role of Syntax During Pronoun Resolution: Evidence from fMRI
Jixing Li | Murielle Fabre | Wen-Ming Luh | John Hale
Proceedings of the Eight Workshop on Cognitive Aspects of Computational Language Learning and Processing

The current study examined the role of syntactic structure during pronoun resolution. We correlated complexity measures derived by the syntax-sensitive Hobbs algorithm and a neural network model for pronoun resolution with brain activity of participants listening to an audiobook during fMRI recording. Compared to the neural network model, the Hobbs algorithm is associated with larger clusters of brain activation in a network including the left Broca’s area.

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Processing MWEs: Neurocognitive Bases of Verbal MWEs and Lexical Cohesiveness within MWEs
Shohini Bhattasali | Murielle Fabre | John Hale
Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018)

Multiword expressions have posed a challenge in the past for computational linguistics since they comprise a heterogeneous family of word clusters and are difficult to detect in natural language data. In this paper, we present a fMRI study based on language comprehension to provide neuroimaging evidence for processing MWEs. We investigate whether different MWEs have distinct neural bases, e.g. if verbal MWEs involve separate brain areas from non-verbal MWEs and if MWEs with varying levels of cohesiveness activate dissociable brain regions. Our study contributes neuroimaging evidence illustrating that different MWEs elicit spatially distinct patterns of activation. We also adapt an association measure, usually used to detect MWEs, as a cognitively plausible metric for language processing.

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LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better
Adhiguna Kuncoro | Chris Dyer | John Hale | Dani Yogatama | Stephen Clark | Phil Blunsom
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Language exhibits hierarchical structure, but recent work using a subject-verb agreement diagnostic argued that state-of-the-art language models, LSTMs, fail to learn long-range syntax sensitive dependencies. Using the same diagnostic, we show that, in fact, LSTMs do succeed in learning such dependencies—provided they have enough capacity. We then explore whether models that have access to explicit syntactic information learn agreement more effectively, and how the way in which this structural information is incorporated into the model impacts performance. We find that the mere presence of syntactic information does not improve accuracy, but when model architecture is determined by syntax, number agreement is improved. Further, we find that the choice of how syntactic structure is built affects how well number agreement is learned: top-down construction outperforms left-corner and bottom-up variants in capturing non-local structural dependencies.

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Finding syntax in human encephalography with beam search
John Hale | Chris Dyer | Adhiguna Kuncoro | Jonathan Brennan
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Recurrent neural network grammars (RNNGs) are generative models of (tree , string ) pairs that rely on neural networks to evaluate derivational choices. Parsing with them using beam search yields a variety of incremental complexity metrics such as word surprisal and parser action count. When used as regressors against human electrophysiological responses to naturalistic text, they derive two amplitude effects: an early peak and a P600-like later peak. By contrast, a non-syntactic neural language model yields no reliable effects. Model comparisons attribute the early peak to syntactic composition within the RNNG. This pattern of results recommends the RNNG+beam search combination as a mechanistic model of the syntactic processing that occurs during normal human language comprehension.

2017

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Entropy Reduction correlates with temporal lobe activity
Matthew Nelson | Stanislas Dehaene | Christophe Pallier | John Hale
Proceedings of the 7th Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2017)

Using the Entropy Reduction incremental complexity metric, we relate high gamma power signals from the brains of epileptic patients to incremental stages of syntactic analysis in English and French. We find that signals recorded intracranially from the anterior Inferior Temporal Sulcus (aITS) and the posterior Inferior Temporal Gyrus (pITG) correlate with word-by-word Entropy Reduction values derived from phrase structure grammars for those languages. In the anterior region, this correlation persists even in combination with surprisal co-predictors from PCFG and ngram models. The result confirms the idea that the brain’s temporal lobe houses a parsing function, one whose incremental processing difficulty profile reflects changes in grammatical uncertainty.

2016

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Temporal Lobes as Combinatory Engines for both Form and Meaning
Jixing Li | Jonathan Brennan | Adam Mahar | John Hale
Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC)

The relative contributions of meaning and form to sentence processing remains an outstanding issue across the language sciences. We examine this issue by formalizing four incremental complexity metrics and comparing them against freely-available ROI timecourses. Syntax-related metrics based on top-down parsing and structural dependency-distance turn out to significantly improve a regression model, compared to a simpler model that formalizes only conceptual combination using a distributional vector-space model. This confirms the view of the anterior temporal lobes as combinatory engines that deal in both form (see e.g. Brennan et al., 2012; Mazoyer, 1993) and meaning (see e.g., Patterson et al., 2007). This same characterization applies to a posterior temporal region in roughly “Wernicke’s Area.”

2015

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Modeling fMRI time courses with linguistic structure at various grain sizes
John Hale | David Lutz | Wen-Ming Luh | Jonathan Brennan
Proceedings of the 6th Workshop on Cognitive Modeling and Computational Linguistics

2010

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Proceedings of the 2010 Workshop on Cognitive Modeling and Computational Linguistics
John T. Hale
Proceedings of the 2010 Workshop on Cognitive Modeling and Computational Linguistics

2009

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Heuristic search in a cognitive model of human parsing
John Hale
Proceedings of the 11th International Conference on Parsing Technologies (IWPT’09)

2008

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Surprising Parser Actions and Reading Difficulty
Marisa Ferrara Boston | John T. Hale | Reinhold Kliegl | Shravan Vasishth
Proceedings of ACL-08: HLT, Short Papers

2006

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SParseval: Evaluation Metrics for Parsing Speech
Brian Roark | Mary Harper | Eugene Charniak | Bonnie Dorr | Mark Johnson | Jeremy Kahn | Yang Liu | Mari Ostendorf | John Hale | Anna Krasnyanskaya | Matthew Lease | Izhak Shafran | Matthew Snover | Robin Stewart | Lisa Yung
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

While both spoken and written language processing stand to benefit from parsing, the standard Parseval metrics (Black et al., 1991) and their canonical implementation (Sekine and Collins, 1997) are only useful for text. The Parseval metrics are undefined when the words input to the parser do not match the words in the gold standard parse tree exactly, and word errors are unavoidable with automatic speech recognition (ASR) systems. To fill this gap, we have developed a publicly available tool for scoring parses that implements a variety of metrics which can handle mismatches in words and segmentations, including: alignment-based bracket evaluation, alignment-based dependency evaluation, and a dependency evaluation that does not require alignment. We describe the different metrics, how to use the tool, and the outcome of an extensive set of experiments on the sensitivity.

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PCFGs with Syntactic and Prosodic Indicators of Speech Repairs
John Hale | Izhak Shafran | Lisa Yung | Bonnie J. Dorr | Mary Harper | Anna Krasnyanskaya | Matthew Lease | Yang Liu | Brian Roark | Matthew Snover | Robin Stewart
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

2004

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The Information-Processing Difficulty of Incremental Parsing
John Hale
Proceedings of the Workshop on Incremental Parsing: Bringing Engineering and Cognition Together

2001

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A Probabilistic Earley Parser as a Psycholinguistic Model
John Hale
Second Meeting of the North American Chapter of the Association for Computational Linguistics

1998

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A Statistical Approach to Anaphora Resolution
Niyu Ge | John Hale | Eugene Charniak
Sixth Workshop on Very Large Corpora