How LSTM Encodes Syntax: Exploring Context Vectors and Semi-Quantization on Natural Text
Chihiro Shibata, Kei Uchiumi, Daichi Mochihashi · 2020
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 showed 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 L 1 regularization, we also found that it can be accurately predicted 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 still shown to correlate well with the phrase structures.Finally, we show that natural clusters of the functional words and the parts of speech that trigger phrases are represented in a small but principal subspace of the context-update vector of LSTM.