Author:
(1) Mingda Chen.
Table of Links
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3.1 Improving Language Representation Learning via Sentence Ordering Prediction
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3.2 Improving In-Context Few-Shot Learning via Self-Supervised Training
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4.2 Learning Discourse-Aware Sentence Representations from Document Structures
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5 DISENTANGLING LATENT REPRESENTATIONS FOR INTERPRETABILITY AND CONTROLLABILITY
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5.1 Disentangling Semantics and Syntax in Sentence Representations
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5.2 Controllable Paraphrase Generation with a Syntactic Exemplar
CHAPTER 5 - DISENTANGLING LATENT REPRESENTATIONS FOR INTERPRETABILITY AND CONTROLLABILITY
In this chapter, we describe our contributions to disentangling latent representations using naturally-occurring structures of paired data. In Section 5.1, we presented a multi-task, latent-variable model that disentangles semantics and syntax in sentence representations. The model leverages the fact that the semantics of a paraphrase pair is shared but syntax varies. In Section 5.2, we extend this framework for controlling the syntax of generated text. In this controlled generation setting, we propose to use a sentential exemplar to control the syntax.
The material in this chapter is adapted from Chen et al. (2019d) and Chen et al. (2019c).
This paper is available on arxiv under CC 4.0 license.