Few-Shot Goal Oriented Dialogue Generation with Knowledge Transfer Networks
By: Anubhav Sachan
Tags: Deep Learning, Artificial Intelligence, Conversational Ai, Neural Networks, Dialog System

This article will help you develop an intuition-based understanding of Goal-Oriented Dialogue Generation in dialogue systems, with Few-Shot training and Knowledge Transfer Networks.

To be specific, we’ll learn about an unsupervised discrete sentence representation learning method that can integrate with any existing encoder-decoder dialogue model, for interpretable response generation using a minimal amount of data that is not annotated.

Why Few-Shot Learning?

Deep Neural Networks have proved to be successful in data-intensive applications. Usually, as shown via conventional practice, a network having numerous parameters has a greater capacity to map the data, and more training data provides the network to have a better generalization.

Such neural networks, in lack of sufficient data, tend to struggle in fixing the weights and biases for the neurons. In contrast, the human brain having much more complex network architecture does not face any difficulty in adapting to new domains. Instead, it excels in learning new concepts with limited data.

Few-shot learning has, therefore, been proposed to close the performance gap between a machine learner and a human learner. In the canonical setting of few-shot learning, there is a known training set and unseen testing set with disjoint categories.

This unique setting of few-shot learning poses an unprecedented challenge in efficiently utilizing the prior information in the training set, which corresponds to the known information or historical information of the human learner.

Hence, NLP researchers leverage the knowledge learned by the main model to improve the performance measure of the architecture on target data.

A Look Inside Few-Shot Dialogue Generation

The architecture of Task-Oriented Multi-turn Dialog System with Reinforcement Learning. Image Source: Medium.

Dialogue generation aims at generating human-like responses given a human-to-human dialogue history. In a conventional task-oriented dialogue system, a response (user’s raw utterance) is taken, via either spoken language understanding (SLU) module or a natural language understanding (NLU) module, and converted to a semantic frame of dialogue acts.

These dialogue acts are sent to a dialogue manager which produces the system’s next action in a semantic frame in accordance with a policy using the dialogue state tracker. Then, through natural language generation (NLG), if chosen by the policy to respond, the semantic frame is transformed into human understandable utterances.

The training of deep learning-based dialogue systems is hugely dependent on large amounts of data, which questions the ability of the system to perform in a real-world environment with limited data.

Hence, ‘few-shot learning’ approaches to data-efficient dialogue system training is introduced for a new domain using a latent dialogue act annotation learned in an unsupervised format from a larger multi-domain data source as proposed by Zhao et al. (2018) (referred to as Latent Action Encoder-Decoder Model).

Latent Action Encoder-Decoder

The development of an unsupervised neural recognition model that can discover interpretable meaning representations of utterances as a set of discrete latent variables can improve the effectiveness of a dialogue system. This is achieved with a better interpretation of the system-intentions and modelization of the high-level decision-making policy that enables useful generalization and data-efficient domain adaptation.

Built upon variational autoencoders (VAEs), DI-VAE and DI-VST discover interpretable semantics via autoencoding and context predicting, respectively. The prime focus lies in learning discrete latent representations instead of dense continuous ones because of their high interpretability.

Discrete Information Variational Autoencoder

Discrete Information Variational Autoencoder (DI-VAE) has been improved upon the traditional Discrete Variational Autoencoder (VAE) through a modification in their learning objective to entertain the anti-information limitation of evidence lower bound objective (ELBO).

Evidence lower bound objective as an expectation over a dataset

The term KL Divergence in ELBO tries to reduce the mutual information (I) between latent variables (Z) and the input data (X) which explains the anti-information limitation.

The resolution regarding the ignorance of the latent variables during the training phase (anti-information limitation) is to maximize both the data likelihood and mutual information between latent action and input.

Joint optimization of ELBO and Mutual Information solves the anti-information limitation in a standard variational autoencoder.

DI-VAE maximizes the ELBO jointly with the mutual information between input data and latent actions. It minimizes the KL Divergence using Batch Prior Regularization which proves to be advantageous over, the usual, Maximum Mean Discrepancy due to its non-linear nature and fundamental difference with annealing.

DI-VAE infers sentence representations by the reconstruction of the input sequence, and hence considered generative.

Discrete Information Variational Skip Thought

The skip thought is a powerful sentence representation that captures contextual information. It uses a Recurrent Neural Network to encode a sentence and then using the resulting representation it predicts the previous and next sentences.

The signals through auto encoding are enriched by extending the concept of skip thought to Discrete Information Variational Skip Thought (DI-VST) that learns sentence level distributional semantics. It uses the same recognition network as DI-VAE to output z’s posterior distribution. The learning objective, as shown, is maximized by the minimization of KL Divergence term.

Learning objective for DI-VST where xₚ represents previous sentence and xₙ as the next sentence.

Integration of DI-VAE and DI-VST with Encoder and Decoders