Abstract
While Long Short-Term Memory networks (LSTMs) and other forms of recurrent neural network have been successfully applied to language modeling on a character level, the hidden state dynamics of these models can be difficult to interpret. We investigate the hidden states of such a model by using the HDBSCAN clustering algorithm to identify points in the text at which the hidden state is similar. Focusing on whitespace characters prior to the beginning of a word reveals interpretable clusters that offer insight into how the LSTM may combine contextual and character-level information to identify parts of speech. We also introduce a method for deriving word vectors from the hidden state representation in order to investigate the word-level knowledge of the model. These word vectors encode meaningful semantic information even for words that appear only once in the training text.- Anthology ID:
- W18-5428
- Volume:
- Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP
- Month:
- November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Tal Linzen, Grzegorz Chrupała, Afra Alishahi
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 258–266
- Language:
- URL:
- https://aclanthology.org/W18-5428
- DOI:
- 10.18653/v1/W18-5428
- Cite (ACL):
- Avery Hiebert, Cole Peterson, Alona Fyshe, and Nishant Mehta. 2018. Interpreting Word-Level Hidden State Behaviour of Character-Level LSTM Language Models. In Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, pages 258–266, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Interpreting Word-Level Hidden State Behaviour of Character-Level LSTM Language Models (Hiebert et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/W18-5428.pdf