Do Encoder Representations of Generative Dialogue Models Encode Sufficient Information about the Task ?

TL;DR

This paper evaluates whether encoder representations in dialogue models contain sufficient task-related information, revealing that RNNs may better preserve task details despite lower text generation metrics compared to Transformers.

Contribution

The paper introduces probe tasks to assess encoder representations in dialogue models, highlighting differences between RNN and Transformer architectures in encoding task-relevant information.

Findings

RNNs outperform Transformers on probe tasks in preserving task information.

Transformers achieve higher scores on automatic text generation metrics.

Some probe tasks are easier or harder for different model architectures.

Abstract

Predicting the next utterance in dialogue is contingent on encoding of users' input text to generate appropriate and relevant response in data-driven approaches. Although the semantic and syntactic quality of the language generated is evaluated, more often than not, the encoded representation of input is not evaluated. As the representation of the encoder is essential for predicting the appropriate response, evaluation of encoder representation is a challenging yet important problem. In this work, we showcase evaluating the text generated through human or automatic metrics is not sufficient to appropriately evaluate soundness of the language understanding of dialogue models and, to that end, propose a set of probe tasks to evaluate encoder representation of different language encoders commonly used in dialogue models. From experiments, we observe that some of the probe tasks are easier and some are harder for even sophisticated model architectures to learn. And, through experiments we observe that RNN based architectures have lower performance on automatic metrics on text generation than transformer model but perform better than the transformer model on the probe tasks indicating that RNNs might preserve task information better than the Transformers.