KERMIT: Generative Insertion-Based Modeling for Sequences

TL;DR

KERMIT introduces a flexible insertion-based generative model for sequences that jointly models data and can perform various inference tasks efficiently, matching or surpassing specialized models across multiple applications.

Contribution

The paper presents KERMIT, a unified neural network approach for sequence modeling that does not require predefined factorization and supports both joint and marginal distribution learning.

Findings

Achieves competitive or superior performance in machine translation and question answering.

Supports both autoregressive and parallel decoding with logarithmic runtime.

Effectively utilizes paired and unpaired data during training.

Abstract

We present KERMIT, a simple insertion-based approach to generative modeling for sequences and sequence pairs. KERMIT models the joint distribution and its decompositions (i.e., marginals and conditionals) using a single neural network and, unlike much prior work, does not rely on a prespecified factorization of the data distribution. During training, one can feed KERMIT paired data $(x, y)$ to learn the joint distribution $p(x, y)$, and optionally mix in unpaired data $x$ or $y$ to refine the marginals $p(x)$ or $p(y)$. During inference, we have access to the conditionals $p(x \mid y)$ and $p(y \mid x)$ in both directions. We can also sample from the joint distribution or the marginals. The model supports both serial fully autoregressive decoding and parallel partially autoregressive decoding, with the latter exhibiting an empirically logarithmic runtime. We demonstrate through experiments in machine translation, representation learning, and zero-shot cloze question answering that our unified approach is capable of matching or exceeding the performance of dedicated state-of-the-art systems across a wide range of tasks without the need for problem-specific architectural adaptation.