{"total":14,"rows":[{"id":"2102.08655","title":"Decoding EEG Brain Activity for Multi-Modal Natural Language Processing","created":"2021-02-17","cited_by_count":13,"area":null},{"id":"2212.12510","title":"MicroBERT: Effective Training of Low-resource Monolingual BERTs through Parameter Reduction and Multitask Learning","created":"2022-12-23","cited_by_count":12,"area":null},{"id":"2210.16848","title":"Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings","created":"2022-10-30","cited_by_count":11,"area":null},{"id":"2103.01009","title":"Snowflake: Scaling GNNs to High-Dimensional Continuous Control via Parameter Freezing","created":"2021-03-01","cited_by_count":7,"area":null},{"id":"2110.08975","title":"Deep Transfer Learning & Beyond: Transformer Language Models in Information Systems Research","created":"2021-10-18","cited_by_count":7,"area":null},{"id":"2107.10413","title":"Theoretical foundations and limits of word embeddings: what types of meaning can they capture?","created":"2021-07-22","cited_by_count":5,"area":null},{"id":"2109.12008","title":"Separating Retention from Extraction in the Evaluation of End-to-end Relation Extraction","created":"2021-09-24","cited_by_count":2,"area":null},{"id":"2310.20620","title":"The Unreasonable Effectiveness of Random Target Embeddings for Continuous-Output Neural Machine Translation","created":"2023-10-31","cited_by_count":2,"area":null},{"id":"2510.13406","title":"When Embedding Models Meet: Procrustes Bounds and Applications","created":"2025-10-15","cited_by_count":2,"area":null},{"id":"2403.13344","title":"USE: Dynamic User Modeling with Stateful Sequence Models","created":"2024-03-20","cited_by_count":1,"area":null},{"id":"2410.13318","title":"Computational Approaches to Arabic-English Code-Switching","created":"2024-10-17","cited_by_count":1,"area":null},{"id":"2102.12266","title":"Trajectory-Based Meta-Learning for Out-Of-Vocabulary Word Embedding Learning","created":"2021-02-24","cited_by_count":0,"area":null},{"id":"2104.05928","title":"Semantic maps and metrics for science Semantic maps and metrics for science using deep transformer encoders","created":"2021-04-13","cited_by_count":0,"area":null},{"id":"2208.09669","title":"Lost in Context? On the Sense-wise Variance of Contextualized Word Embeddings","created":"2022-08-20","cited_by_count":0,"area":null}],"next_cursor":null}