Improving Disentangled Text Representation Learning with Information-Theoretic Guidance (2020)

Abstract: Learning disentangled representations of natural language is essential for many NLP tasks, e.g., conditional text generation, style transfer, personalized dialogue systems, etc. Similar problems have been studied extensively for other forms of data, such as images and videos. However, the discrete nature of natural language makes the disentangling of textual representations more challenging (e.g., the manipulation over the data space cannot be easily achieved). Inspired by information theory, we propose a novel method that effectively manifests disentangled representations of text, without any supervision on semantics. A new mutual information upper bound is derived and leveraged to measure dependence between style and content. By minimizing this upper bound, the proposed method induces style and content embeddings into two independent low-dimensional spaces. Experiments on both conditional text generation and text-style transfer demonstrate the high quality of our disentangled representation in terms of content and style preservation.

## introduction disentangled representation learning (drl), which maps different aspects of data into distinct and independent low-dimensional latent vector spaces, has attracted considerable attention for making deep learning models more interpretable. through a series of operations such as selecting, combining, and switching, the learned disentangled representations can be utilized for downstream tasks, such as domain adaptation @xcite , style transfer @xcite , conditional generation @xcite , and few-shot learning @xcite . although widely used in various domains, such as images @xcite , videos @xcite , and speech @xcite , many challenges in drl have received limited exploration in natural language processing @xcite . to disentangle various attributes of text, two distinct types of embeddings are typically considered: the style embedding and the content embedding @xcite . the content embedding is designed to encapsulate the semantic meaning of a sentence. in contrast, the style embedding should represent desired attributes, such as the sentiment of a review, or the personality associated with a post. ideally, a disentangled-text-representation model should learn representative embeddings for both style and content. to accomplish this, several strategies have been introduced. @xcite proposed to learn a semantically-meaningful content embedding space by matching the content embedding from two different style domains. however, their method requires predefined style domains, and thus cannot automatically infer style information from unlabeled text. @xcite and @xcite utilized one-hot vectors as style-related features (instead of inferring the style embeddings from the original data). these models are not applicable when new data comes from an unseen style class. @xcite proposed an encoderdecoder model in combination with an adversarial training objective to infer both style and content embeddings from the original data. however, their adversarial training framework requires manually-processed supervised information for content embeddings (e.g., reconstructing sentences with manually-chosen sentiment-related words removed). further, there is no theoretical guarantee for the quality of disentanglement. in this paper, we introduce a novel information-theoretic disentangled embedding learning method (idel) for text, based on guidance from information theory. inspired by variation of information (vi), we introduce a novel informationtheoretic objective to measure how well the learned representations are disentangled. specifically, our idel reduces the dependency between style and content embeddings by minimizing a sample-based mutual information upper bound. furthermore, the mutual information between latent embeddings and the input data is also maximized to ensure the representativeness of the latent embeddings (i.e., style and content embeddings). the contributions of this paper are summarized as follows: ## method consider data {(x i , y i )} n @xmath0, where each x i is a sentence drawn from a distribution p(x), and y i is the label indicating the style of x i . the goal is to encode each sentence x i into its corresponding style embedding s i and content embedding c i with an encoder q θ (s, c|x): the collection of style embeddings {s i } n @xmath1, while the collection of content embeddings {c i } n @xmath2. in practice, the dimension of the content embedding is typically higher than that of the style embedding, considering that the content usually contains more information than the style @xcite . we first give an intuitive introduction to our proposed vi-based objective, then in section 3.1 we provide the theoretical justification for it. to disentangle the style and content embedding, we try to minimize the mutual information i(s; c) between s and c. meanwhile, we maximize i(c; x) to ensure that the content embedding s sufficiently encapsulates information from the sentence x. the embedding s is expected to contain rich style information. therefore, the mutual information i(s; y) should be maximized. thus, our overall disentangled representation learning objective is: l @xmath3; c) -i(c; x) -i(s; y). ## conclusions we have proposed a novel information-theoretic disentangled text representation learning framework. following the theoretical guidance from information theory, our method separates the textual information into independent spaces, constituting style and content representations. a sample-based mutual information upper bound is derived to help reduce the dependence between embedding spaces.