Search Results for author:
Found 12 papers, 7 papers with code
MedReadMe: A Systematic Study for Fine-grained Sentence Readability in Medical Domain
Medical texts are notoriously challenging to read.
Successfully Applying Lottery Ticket Hypothesis to Diffusion Model
Therefore, we propose to find the winning ticket with varying sparsity along different layers in the model.
Frustratingly Easy Label Projection for Cross-lingual Transfer
Translating training data into many languages has emerged as a practical solution for improving cross-lingual transfer.
Ranked #1 on
Cross-Lingual NER
on MasakhaNER2.0
arXivEdits: Understanding the Human Revision Process in Scientific Writing
We first introduce arXivEdits, a new annotated corpus of 751 full papers from arXiv with gold sentence alignment across their multiple versions of revision, as well as fine-grained span-level edits and their underlying intentions for 1, 000 sentence pairs.
Improving Large-scale Paraphrase Acquisition and Generation
This paper addresses the quality issues in existing Twitter-based paraphrase datasets, and discusses the necessity of using two separate definitions of paraphrase for identification and generation tasks.
Densely connected neural networks for nonlinear regression
Densely connected convolutional networks (DenseNet) behave well in image processing.
Neural semi-Markov CRF for Monolingual Word Alignment
Monolingual word alignment is important for studying fine-grained editing operations (i. e., deletion, addition, and substitution) in text-to-text generation tasks, such as paraphrase generation, text simplification, neutralizing biased language, etc.
Neural CRF Model for Sentence Alignment in Text Simplification
The success of a text simplification system heavily depends on the quality and quantity of complex-simple sentence pairs in the training corpus, which are extracted by aligning sentences between parallel articles.
Ranked #1 on
Text Simplification
on Newsela
Discourse Level Factors for Sentence Deletion in Text Simplification
We inspect various document and discourse factors associated with sentence deletion, using a new manually annotated sentence alignment corpus we collected.
Learning Word Embeddings for Low-Resource Languages by PU Learning
In such a situation, the co-occurrence matrix is sparse as the co-occurrences of many word pairs are unobserved.
LearningWord Embeddings for Low-resource Languages by PU Learning
In such a situation, the co-occurrence matrix is sparse as the co-occurrences of many word pairs are unobserved.
Multi-task Learning for Universal Sentence Embeddings: A Thorough Evaluation using Transfer and Auxiliary Tasks
Learning distributed sentence representations is one of the key challenges in natural language processing.
