Cheng-Syuan Lee


Explaining Word Embeddings via Disentangled Representation
Keng-Te Liao | Cheng-Syuan Lee | Zhong-Yu Huang | Shou-de Lin
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing

Disentangled representations have attracted increasing attention recently. However, how to transfer the desired properties of disentanglement to word representations is unclear. In this work, we propose to transform typical dense word vectors into disentangled embeddings featuring improved interpretability via encoding polysemous semantics separately. We also found the modular structure of our disentangled word embeddings helps generate more efficient and effective features for natural language processing tasks.


Word Relation Autoencoder for Unseen Hypernym Extraction Using Word Embeddings
Hong-You Chen | Cheng-Syuan Lee | Keng-Te Liao | Shou-De Lin
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Lexicon relation extraction given distributional representation of words is an important topic in NLP. We observe that the state-of-the-art projection-based methods cannot be generalized to handle unseen hypernyms. We propose to analyze it in the perspective of pollution and construct the corresponding indicator to measure it. We propose a word relation autoencoder (WRAE) model to address the challenge. Experiments on several hypernym-like lexicon datasets show that our model outperforms the competitors significantly.