Transactions of the Association for Computational Linguistics

Papers
(The H4-Index of Transactions of the Association for Computational Linguistics is 22. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2021-04-01 to 2025-04-01.)
ArticleCitations
QAmeleon: Multilingual QA with Only 5 Examples158
Salute the Classic: Revisiting Challenges of Machine Translation in the Age of Large Language Models150
Neuron-level Interpretation of Deep NLP Models: A Survey96
CreoleVal: Multilingual Multitask Benchmarks for Creoles81
Segmentation-Free Streaming Machine Translation80
A Neighborhood Framework for Resource-Lean Content Flagging74
On Graph-based Reentrancy-free Semantic Parsing72
Explicitly Representing Syntax Improves Sentence-to-Layout Prediction of Unexpected Situations64
Evaluating Transformer Models and Human Behaviors on Chinese Character Naming60
Cross-functional Analysis of Generalization in Behavioral Learning59
Data-driven Model Generalizability in Crosslinguistic Low-resource Morphological Segmentation55
A Cross-Linguistic Pressure for Uniform Information Density in Word Order51
Hallucinations in Large Multilingual Translation Models42
Multi-task Active Learning for Pre-trained Transformer-based Models38
Direct Speech Translation for Automatic Subtitling32
The Impact of Word Splitting on the Semantic Content of Contextualized Word Representations30
OpenFact: Factuality Enhanced Open Knowledge Extraction27
Diff-Explainer: Differentiable Convex Optimization for Explainable Multi-hop Inference26
Tracking Brand-Associated Polarity-Bearing Topics in User Reviews26
Reasoning over Public and Private Data in Retrieval-Based Systems23
Learning Syntax Without Planting Trees: Understanding Hierarchical Generalization in Transformers22
Deuce: Dual-diversity Enhancement and Uncertainty-awareness for Cold-start Active Learning22
AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning22
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