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20202026
most citedA Theoretical Analysis of the Repetition Problem in Text Generation

5 citations · 11 across the 6 of their papers we have counts for

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9 papers · 1 filter

cs.CL20233 cited

Repetition In Repetition Out: Towards Understanding Neural Text Degeneration from the Data Perspective

Huayang Li, Tian Lan, Zihao Fu +5

There are a number of diverging hypotheses about the neural text degeneration problem, i.e., generating repetitive and dull loops, which makes this problem both interesting and con…

cs.CL2023

BAND: Biomedical Alert News Dataset

Zihao Fu, Meiru Zhang, Zaiqiao Meng +3

Infectious disease outbreaks continue to pose a significant threat to human health and well-being. To improve disease surveillance and understanding of disease spread, several surv…

cs.CL2023

Biomedical Named Entity Recognition via Dictionary-based Synonym Generalization

Zihao Fu, Yixuan Su, Zaiqiao Meng +1

Biomedical named entity recognition is one of the core tasks in biomedical natural language processing (BioNLP). To tackle this task, numerous supervised/distantly supervised appro…

cs.CL202316 cited

Decoder-Only or Encoder-Decoder? Interpreting Language Model as a Regularized Encoder-Decoder

Zihao Fu, Wai Lam, Qian Yu +4

The sequence-to-sequence (seq2seq) task aims at generating the target sequence based on the given input source sequence. Traditionally, most of the seq2seq task is resolved by the…

cs.CL2023

COFFEE: A Contrastive Oracle-Free Framework for Event Extraction

Meiru Zhang, Yixuan Su, Zaiqiao Meng +2

Event extraction is a complex information extraction task that involves extracting events from unstructured text. Prior classification-based methods require comprehensive entity an…

cs.CL20221 cited

On the Effectiveness of Parameter-Efficient Fine-Tuning

Zihao Fu, Haoran Yang, Anthony Man-Cho So +3

Fine-tuning pre-trained models has been ubiquitously proven to be effective in a wide range of NLP tasks. However, fine-tuning the whole model is parameter inefficient as it always…