# How LSTM Encodes Syntax: Exploring Context Vectors and Semi-Quantization on Natural Text

arXiv: https://arxiv.org/abs/2010.00363 · HTML: https://mlsanity.com/paper/2010.00363/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2020.

## Built on by (most cited first, 2 of 2)

- [Formal Language Theory Meets Modern NLP](https://mlsanity.com/paper/2102.10094/) (2021)
- [Tracing and Manipulating Intermediate Values in Neural Math Problem Solvers](https://mlsanity.com/paper/2301.06758/) (2023)

## Abstract (arXiv)

Long Short-Term Memory recurrent neural network (LSTM) is widely used and known to capture informative long-term syntactic dependencies. However, how such information are reflected in its internal vectors for natural text has not yet been sufficiently investigated. We analyze them by learning a language model where syntactic structures are implicitly given. We empirically show that the context update vectors, i.e. outputs of internal gates, are approximately quantized to binary or ternary values to help the language model to count the depth of nesting accurately, as Suzgun et al. (2019) recently show for synthetic Dyck languages. For some dimensions in the context vector, we show that their activations are highly correlated with the depth of phrase structures, such as VP and NP. Moreover, with an L1 regularization, we also found that it can accurately predict whether a word is inside a phrase structure or not from a small number of components of the context vector. Even for the case of learning from raw text, context vectors are shown to still correlate well with the phrase structures. Finally, we show that natural clusters of the functional words and the part of speeches that trigger phrases are represented in a small but principal subspace of the context-update vector of LSTM.