Publications

2021

  1. Li R.*, Peng X.*, Lin C. On the Latent Holes 🧀 of VAEs for Text Generation, arXiv:2110.03318, 2021.
    [pdf]

  2. Zeng C., Chen G., Lin C., Li R. and Chen Z. Affective Decoding for Empathetic Response Generation, The 14th International Conference on Natural Language Generation (INLG), 2021.
    [pdf] [code] [BibTex]

  3. Li R.*, Peng X.*, Lin C., Rong W. and Chen Z. On the Low-density Latent Regions of VAE-based Language Models, NeurIPS 2020 Workshop on Pre-registration in Machine Learning, PMLR 148:343-357, 2021.
    [pdf] [BibTex]

2020

  1. Li R., Li X., Chen G. and Lin C. Improving Variational Autoencoder for Text Modelling with Timestep-Wise Regularisation, The 28th International Conference on Computational Linguistics (COLING), 2020.
    [pdf] [code] [BibTex]
  2. Li X., Chen G., Lin C. and Li R. DGST: a Dual-Generator Network for Text Style Transfer, Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020.
    [pdf] [code] [BibTex]
  3. Li X., Lin C., Li R., Wang C. and Guerin F. Latent Space Factorisation and Manipulation via Matrix Subspace Projection, The 37th International Conference on Machine Learning (ICML), Vienna, 2020.
    [pdf] [code] [BibTex]

2019

  1. Li R., Lin C., Collinson M., Li X. and Chen G. A Dual-Attention Hierarchical Recurrent Neural Network for Dialogue Act Classification, The SIGNLL Conference on Computational Natural Language Learning (CoNLL), Hong Kong, China, 2019.
    [pdf] [BibTex]
  2. Li R., Li X., Lin C, Collinson M. and Mao R. A Stable Variational Autoencoder for Text Modelling, The 12th International Conference on Natural Language Generation (INLG), Tokyo, 2019.
    [pdf] [code] [BibTex]

2018

  1. Mao R., Chen G., Li, R. and Lin C. ABDN at SemEval-2018 Task 10: Recognising Discriminative Attributes using Context Embeddings and WordNet. The International Workshop on Semantic Evaluation at the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), New Orleans, 2018.
    [pdf] [BibTex]

Thesis

Li, R. Deep Latent Variable Models for Text Modelling, University of Sheffield, 2021.
[pdf]