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Found 22 papers, 6 papers with code
Dual Mechanism Priming Effects in Hindi Word Order
By showing that different priming influences are separable from one another, our results support the hypothesis that multiple different cognitive mechanisms underlie priming.
Discourse Context Predictability Effects in Hindi Word Order
While prior work has shown that a number of factors (e. g., information status, dependency length, and syntactic surprisal) influence Hindi word order preferences, the role of discourse predictability is underexplored in the literature.
All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality
Moreover, we find a striking mismatch between the dimensions that dominate similarity measures and those which are important to the behavior of the model.
To Point or Not to Point: Understanding How Abstractive Summarizers Paraphrase Text
Abstractive neural summarization models have seen great improvements in recent years, as shown by ROUGE scores of the generated summaries.
Uncovering Constraint-Based Behavior in Neural Models via Targeted Fine-Tuning
We show that competing processes in a language act as constraints on model behavior and demonstrate that targeted fine-tuning can re-weight the learned constraints, uncovering otherwise dormant linguistic knowledge in models.
Filler-gaps that neural networks fail to generalize
We use cumulative priming to test for representational overlap between disparate filler-gap constructions in English and find evidence that the models learn a general representation for the existence of filler-gap dependencies.
Discourse structure interacts with reference but not syntax in neural language models
Language models (LMs) trained on large quantities of text have been claimed to acquire abstract linguistic representations.
Recurrent Neural Network Language Models Always Learn English-Like Relative Clause Attachment
A standard approach to evaluating language models analyzes how models assign probabilities to valid versus invalid syntactic constructions (i. e. is a grammatical sentence more probable than an ungrammatical sentence).
Using Priming to Uncover the Organization of Syntactic Representations in Neural Language Models
Neural language models (LMs) perform well on tasks that require sensitivity to syntactic structure.
Quantity doesn't buy quality syntax with neural language models
We investigate to what extent these shortcomings can be mitigated by increasing the size of the network and the corpus on which it is trained.
Can Entropy Explain Successor Surprisal Effects in Reading?
Human reading behavior is sensitive to surprisal: more predictable words tend to be read faster.
A Neural Model of Adaptation in Reading
It has been argued that humans rapidly adapt their lexical and syntactic expectations to match the statistics of the current linguistic context.
Addressing surprisal deficiencies in reading time models
This study demonstrates a weakness in how n-gram and PCFG surprisal are used to predict reading times in eye-tracking data.
Memory access during incremental sentence processing causes reading time latency
Studies on the role of memory as a predictor of reading time latencies (1) differ in their predictions about when memory effects should occur in processing and (2) have had mixed results, with strong positive effects emerging from isolated constructed stimuli and weak or even negative effects emerging from naturally-occurring stimuli.
