arXiv
Open Access
2023
Unsupervised Lexical Simplification with Context Augmentation
Takashi Wada
Timothy Baldwin
Jey Han Lau
Abstrak
We propose a new unsupervised lexical simplification method that uses only monolingual data and pre-trained language models. Given a target word and its context, our method generates substitutes based on the target context and also additional contexts sampled from monolingual data. We conduct experiments in English, Portuguese, and Spanish on the TSAR-2022 shared task, and show that our model substantially outperforms other unsupervised systems across all languages. We also establish a new state-of-the-art by ensembling our model with GPT-3.5. Lastly, we evaluate our model on the SWORDS lexical substitution data set, achieving a state-of-the-art result.
Penulis (3)
T
Takashi Wada
T
Timothy Baldwin
J
Jey Han Lau
Akses Cepat
Informasi Jurnal
- Tahun Terbit
- 2023
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓