Papers with information transfer

5 papers
In Neural Machine Translation, What Does Transfer Learning Transfer? (2020.acl-main)

Copied to clipboard

Challenge: a recent study found that word embeddings are not necessary for transfer learning.
Approach: They perform several ablation studies that limit information transfer and measure the quality impact across three language pairs to gain a black-box understanding of transfer learning.
Outcome: The proposed method can eliminate the need for a warm-up phase when training transformer models in high resource language pairs.
INT: Establishing Information Transfer for Multilingual Intent Detection and Slot Filling (2025.findings-acl)

Copied to clipboard

Challenge: Existing studies struggle to achieve performance comparable to that on high-resource languages due to inherent linguistic diversity of multilingual SLU tasks.
Approach: They propose a multilingual information transfer network to solve these challenges . they propose to reformulate SF as a span prediction problem and introduce a slot-matching attention mechanism to achieve slot alignment across languages.
Outcome: The proposed model outperforms baseline models on the MASSIVE and MASSIV-UG datasets in overall accuracy across all languages.
mPresenter: An Agentic Framework for Generating Multilingual Presentation Videos from Scientific Papers (2026.findings-acl)

Copied to clipboard

Challenge: Existing Paper2Video systems are monolingual and often rely on single-pass pipelines.
Approach: They propose a multilingual agentic Paper2Video system that decomposes the task into planning, audience-oriented critique, layout-aware slide generation, and multilingual figure interpretation.
Outcome: The proposed system improves question-answering accuracy relative to previous systems while maintaining affordable cost and latency.
Leveraging Social Context for Humor Recognition and Sense of Humor Evaluation in Social Media with a New Chinese Humor Corpus - HumorWB (2024.lrec-main)

Copied to clipboard

Challenge: Existing humor computing research focuses on content while neglecting interaction relationships in social media.
Approach: They propose a dataset which introduces social context information from social media . they propose 'humor recognition' task and 'horror evaluation task'
Outcome: The proposed model incorporates social context information from social media . it shows that it is efficient and can be used to evaluate humor in real life .
All for One: LLMs Solve Mental Math at the Last Token With Information Transferred From Other Tokens (2025.emnlp-main)

Copied to clipboard

Challenge: Large language models (LLMs) perform well on a multitude of computational tasks, yet their inner workings remain unclear.
Approach: They propose two techniques to inhibit input-specific token computations in initial layers . they propose a transformer that allows for any token to immediately access all preceding tokens .
Outcome: The proposed algorithms can perform on a variety of mental math tasks with high accuracy and transfer across models.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations