Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses (1708.07149v2)

Abstract: Automatically evaluating the quality of dialogue responses for unstructured domains is a challenging problem. Unfortunately, existing automatic evaluation metrics are biased and correlate very poorly with human judgements of response quality. Yet having an accurate automatic evaluation procedure is crucial for dialogue research, as it allows rapid prototyping and testing of new models with fewer expensive human evaluations. In response to this challenge, we formulate automatic dialogue evaluation as a learning problem. We present an evaluation model (ADEM) that learns to predict human-like scores to input responses, using a new dataset of human response scores. We show that the ADEM model's predictions correlate significantly, and at a level much higher than word-overlap metrics such as BLEU, with human judgements at both the utterance and system-level. We also show that ADEM can generalize to evaluating dialogue models unseen during training, an important step for automatic dialogue evaluation.

An Exploration of Automated Dialogue Response Evaluation

The paper "Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses" presents a novel approach to evaluating dialogue responses automatically. The challenge of effectively assessing dialogue systems, especially those trained for unstructured domains, is well acknowledged, as existing automatic evaluation metrics like BLEU and METEOR poorly correlate with human judgments. This research introduces an evaluation model named ADEM (Automatic Dialogue Evaluation Model) that learns to align its assessments closely with human evaluation by leveraging a dataset of human-assigned response scores.

Key Contributions

The core contribution of this work is the formulation of dialogue response evaluation as a learning problem. ADEM utilizes a hierarchical recurrent neural network (RNN) to encode dialogue contexts and responses. Unlike traditional word-overlap metrics, ADEM incorporates both the context of the conversation and the reference response, allowing it to assess the appropriateness of a model-generated response more robustly.

Strong Numerical Results:

• ADEM's predictions demonstrated significantly higher correlation with human judgments than BLEU and other word-overlap metrics at both the utterance-level and system-level evaluation.
• It was able to generalize its evaluations to dialogue models that were not seen during its training phase, a critical capability for scalable evaluation.

Implications and Theoretical Insights

The research signifies progress in the pursuit of an automatic Turing test. By formulating evaluation through a learned model, the authors address the inadequacies of existing metrics in capturing semantic relevance and appropriateness beyond surface-level word overlaps. This approach, grounded in machine learning, moves towards more reliable, scalable, and less labor-intensive dialogue system evaluation.

Practical Implications:

• The adoption of ADEM can significantly reduce the reliance on expensive human evaluations, expediting the iterative development of dialogue systems.
• Its capacity to generalize across unseen models suggests applicability across various conversational agents, further broadening its utility in the industry.

Theoretical Implications:

• Introducing machine learning principles into dialogue evaluation challenges conventional reliance on static, handcrafted evaluation methodologies, potentially steering future research towards data-driven evaluation mechanisms.
• ADEM's robust correlation with human evaluators brings us a step closer to automating the evaluation process of dialogue systems comparable to human assessment fidelity.

Future Prospects in AI

This research opens several avenues for future exploration. Increasing ADEM's adaptability to diverse conversational domains and integrating it into reinforcement learning frameworks can foster the development of dialogue systems that not only mimic human interaction but also evolve through automated self-improvement. Future studies could explore extending ADEM to multi-turn dialogues, aiming for more holistic evaluation metrics that consider engagement, coherence, and long-term user satisfaction.

In conclusion, this paper's contributions highlight the potential of leveraging machine learning for evaluating dialogue systems. ADEM represents a promising direction towards automated, scalable evaluation methods that closely correlate with human judgment, bringing new insights into the challenges of designing conversational AI.