Search Results for author:
Found 24 papers, 15 papers with code
Attention is Not Only a Weight: Analyzing Transformers with Vector Norms
Attention is a key component of Transformers, which have recently achieved considerable success in natural language processing.
Incorporating Residual and Normalization Layers into Analysis of Masked Language Models
Transformer architecture has become ubiquitous in the natural language processing field.
Analyzing Feed-Forward Blocks in Transformers through the Lens of Attention Maps
Transformers are ubiquitous in wide tasks.
TEASPN: Framework and Protocol for Integrated Writing Assistance Environments
Language technologies play a key role in assisting people with their writing.
Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognition
Interpretable rationales for model predictions play a critical role in practical applications.
Diamonds in the Rough: Generating Fluent Sentences from Early-Stage Drafts for Academic Writing Assistance
The writing process consists of several stages such as drafting, revising, editing, and proofreading.
Lower Perplexity is Not Always Human-Like
Overall, our results suggest that a cross-lingual evaluation will be necessary to construct human-like computational models.
Context Limitations Make Neural Language Models More Human-Like
Language models (LMs) have been used in cognitive modeling as well as engineering studies -- they compute information-theoretic complexity metrics that simulate humans' cognitive load during reading.
Feasible Annotation Scheme for Capturing Policy Argument Reasoning using Argument Templates
Most of the existing works on argument mining cast the problem of argumentative structure identification as classification tasks (e. g. attack-support relations, stance, explicit premise/claim).
Do Deep Neural Networks Capture Compositionality in Arithmetic Reasoning?
Compositionality is a pivotal property of symbolic reasoning.
An Empirical Study of Span Representations in Argumentation Structure Parsing
For several natural language processing (NLP) tasks, span representation design is attracting considerable attention as a promising new technique; a common basis for an effective design has been established.
Language Models as an Alternative Evaluator of Word Order Hypotheses: A Case Study in Japanese
We examine a methodology using neural language models (LMs) for analyzing the word order of language.
Langsmith: An Interactive Academic Text Revision System
Despite the current diversity and inclusion initiatives in the academic community, researchers with a non-native command of English still face significant obstacles when writing papers in English.
Modeling Event Salience in Narratives via Barthes' Cardinal Functions
Events in a narrative differ in salience: some are more important to the story than others.
Instance-Based Neural Dependency Parsing
Interpretable rationales for model predictions are crucial in practical applications.
Does Vision Accelerate Hierarchical Generalization of Neural Language Learners?
Neural language models (LMs) are arguably less data-efficient than humans -- why does this gap occur?
Empirical Investigation of Neural Symbolic Reasoning Strategies
Neural reasoning accuracy improves when generating intermediate reasoning steps.
Transformer Language Models Handle Word Frequency in Prediction Head
Prediction head is a crucial component of Transformer language models.
Second Language Acquisition of Neural Language Models
With the success of neural language models (LMs), their language acquisition has gained much attention.
Assessing Step-by-Step Reasoning against Lexical Negation: A Case Study on Syllogism
Large language models (LLMs) take advantage of step-by-step reasoning instructions, e. g., chain-of-thought (CoT) prompting.
Psychometric Predictive Power of Large Language Models
In other words, pure next-word probability remains a strong predictor for human reading behavior, even in the age of LLMs.
Emergent Word Order Universals from Cognitively-Motivated Language Models
This also showcases the advantage of cognitively-motivated LMs, which are typically employed in cognitive modeling, in the computational simulation of language universals.
To Drop or Not to Drop? Predicting Argument Ellipsis Judgments: A Case Study in Japanese
Speakers sometimes omit certain arguments of a predicate in a sentence; such omission is especially frequent in pro-drop languages.
