Evaluating Coherence in Dialogue Systems using Entailment (1904.03371v2)

Abstract: Evaluating open-domain dialogue systems is difficult due to the diversity of possible correct answers. Automatic metrics such as BLEU correlate weakly with human annotations, resulting in a significant bias across different models and datasets. Some researchers resort to human judgment experimentation for assessing response quality, which is expensive, time consuming, and not scalable. Moreover, judges tend to evaluate a small number of dialogues, meaning that minor differences in evaluation configuration may lead to dissimilar results. In this paper, we present interpretable metrics for evaluating topic coherence by making use of distributed sentence representations. Furthermore, we introduce calculable approximations of human judgment based on conversational coherence by adopting state-of-the-art entailment techniques. Results show that our metrics can be used as a surrogate for human judgment, making it easy to evaluate dialogue systems on large-scale datasets and allowing an unbiased estimate for the quality of the responses.

Evaluating Coherence in Dialogue Systems using Entailment: An Expert Overview

The paper "Evaluating Coherence in Dialogue Systems using Entailment" by Dziri et al. presents a novel approach to assessing the coherence in dialogue systems. This research addresses significant challenges in open-domain dialogue evaluation by exploiting Natural Language Inference (NLI) techniques to improve the alignment between automated metrics and human annotations. Traditional metrics such as BLEU have demonstrated weak correlation with human judgment, necessitating the development of more sophisticated and interpretable measures.

The authors propose an innovative framework wherein the coherence of a dialogue system is expressed as an NLI task. Specifically, a generated response is cast as a hypothesis and the conversation history as a premise. This conversion allows for an evaluation of response coherence as an entailment problem, leveraging state-of-the-art NLI models such as ESIM augmented with ELMo embeddings and BERT. The core premise of this methodology is to train these models on a newly synthesized inference dataset derived from the Persona-Chat dataset, termed InferConvAI, which comprises premise-hypothesis pairs annotated as entailment, neutral, or contradiction.

Experimentally, the authors trained different dialogue generation models, including Seq2Seq, HRED, TA-Seq2Seq, and THRED, using Reddit and OpenSubtitles datasets, subsequently evaluating the responses using both automated metrics and human judgment. Their evaluation revealed that entailment-based metrics provided a robust measure of dialogue coherence, outperforming traditional word-level similarity metrics and correlating significantly with human evaluations. For instance, BERT outperformed ESIM in determining response coherence, evidencing the adaptability of transformer-based models to the task.

The research underscores the feasibility of employing entailment models to detect logical inconsistencies in dialogue systems, thereby offering a scalable, unbiased evaluation method not reliant on costly human annotations. Table 1 in the paper indicates a substantial volume of 1.1 million premise-hypothesis pairs in the InferConvAI dataset, underscoring the comprehensive nature of the training corpus. Notably, this work builds a foundation for future exploration into automated metrics that can encompass nuanced aspects of human conversation, such as engagingness, which are currently less quantifiable by existing systems.

From a practical perspective, the development of reliable coherence metrics should noticeably enhance the capability of dialogue systems, improving user interaction by ensuring consistency and relevancy in multi-turn conversations. Theoretically, this work contributes to the broader discourse on semantic understanding in LLMs, potentially influencing future research directions towards more integrated and adaptive evaluation frameworks in AI.

In conclusion, Dziri et al.'s contribution is significant for its methodological innovation and practical implications, pushing the boundaries of dialogue evaluation by marrying NLI techniques with conversational AI. This paper not only illustrates the strengths of entailment models in assessing dialogue coherence but also serves as a stepping stone for further advancements in automated evaluation methodologies.