In natural human-to-human conversations, participants often receive feedback signals from one another based on their follow-up reactions. These reactions can include verbal responses, facial expressions, changes in emotional state, and other non-verbal cues. Similarly, in human-machine interactions, the machine can leverage the user's follow-up utterances as feedback signals to assess whether it has appropriately addressed the user's request. Therefore, we propose using the likelihood of follow-up utterances as rewards to differentiate preferred responses from less favored ones, without relying on human or commercial LLM-based preference annotations. Our proposed reward mechanism, ``Follow-up Likelihood as Reward" (FLR), matches the performance of strong reward models trained on large-scale human or GPT-4 annotated data on 8 pairwise-preference and 4 rating-based benchmarks. Building upon the FLR mechanism, we propose to automatically mine preference data from the online generations of a base policy model. The preference data are subsequently used to boost the helpfulness of the base model through direct alignment from preference (DAP) methods, such as direct preference optimization (DPO). Lastly, we demonstrate that fine-tuning the language model that provides follow-up likelihood with natural language feedback significantly enhances FLR's performance on reward modeling benchmarks and effectiveness in aligning the base policy model's helpfulness.

Chatbots are expected to be knowledgeable across multiple domains, e.g. for daily chit-chat, exchange of information, and grounding in emotional situations. To effectively measure the quality of such conversational agents, a model-based automatic dialogue evaluation metric (ADEM) is expected to perform well across multiple domains. Despite significant progress, existing ADEMs tend to perform well only on data that are similar to its training data (overfit to its training domain). This calls for a domain-generalized metric that can assess dialogues of different characteristics. To this end, we propose aPanel of Experts(PoE), a multitask network that consists of a shared transformer encoder and a collection of lightweight adapters. The shared encoder captures the general knowledge of dialogues across domains, while each adapter specializes in one specific domain and serves as a domain expert. To validate the idea, we construct a high-quality multi-domain dialogue dataset leveraging data augmentation and pseudo-labeling. The PoE network is comprehensively assessed on 16 dialogue evaluation datasets spanning a wide range of dialogue domains. It achieves state-of-the-art performance in terms of mean Spearman correlation over all the evaluation datasets. It exhibits better zero-shot generalization than existing state-of-the-art ADEMs and the ability to easily adapt to new domains with few-shot transfer learning.

Chatbots are designed to carry out human-like conversations across different domains, such as general chit-chat, knowledge exchange, and persona-grounded conversations. To measure the quality of such conversational agents, a dialogue evaluator is expected to conduct assessment across domains as well. However, most of the state-of-the-art automatic dialogue evaluation metrics (ADMs) are not designed for multi-domain evaluation. We are motivated to design a general and robust framework, MDD-Eval, to address the problem. Specifically, we first train a teacher evaluator with human-annotated data to acquire a rating skill to tell good dialogue responses from bad ones in a particular domain and then, adopt a self-training strategy to train a new evaluator with teacher-annotated multi-domain data, that helps the new evaluator to generalize across multiple domains. MDD-Eval is extensively assessed on six dialogue evaluation benchmarks. Empirical results show that the MDD-Eval framework achieves a strong performance with an absolute improvement of 7% over the state-of-the-art ADMs in terms of mean Spearman correlation scores across all the evaluation benchmarks.

Recent model-based reference-free metrics for open-domain dialogue evaluation exhibit promising correlations with human judgment 1 .However, they either perform turn-level evaluation or look at a single dialogue quality dimension.One would expect a good evaluation metric to assess multiple quality dimensions at the dialogue level.To this end, we are motivated to propose a multi-dimensional dialogue-level metric, which consists of three sub-metrics with each targeting a specific dimension.The submetrics are trained with novel self-supervised objectives and exhibit strong correlations with human judgment for their respective dimensions.Moreover, we explore two approaches to combine the sub-metrics: metric ensemble and multitask learning.Both approaches yield a holistic metric that significantly outperforms individual sub-metrics.Compared to the existing state-of-the-art metric, the combined metrics achieve around 16% relative improvement on average across three high-quality dialoguelevel evaluation benchmarks.

Chen Zhang, Yiming Chen, Luis Fernando D’Haro, Yan Zhang, Thomas Friedrichs, Grandee Lee, Haizhou Li. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

In artistic gymnastics, difficulty score or D-score is used for judging performance. Starting from zero, an athlete earns points from different aspects such as composition requirement, difficulty, and connection between moves. The final score is a composition of the quality of various performance indicators. Similarly, when evaluating dialogue responses, human judges generally follow a number of criteria, among which language fluency, context coherence, logical consistency, and semantic appropriateness are on top of the agenda. In this paper, we propose an automatic dialogue evaluation framework called D-score that resembles the way gymnastics is evaluated. Following the four human judging criteria above, we devise a range of evaluation tasks and model them under a multi-task learning framework. The proposed framework, without relying on any human-written reference, learns to appreciate the overall quality of human-human conversations through a representation that is shared by all tasks without over-fitting to individual task domain. We evaluate D-score by performing comprehensive correlation analyses with human judgement on three dialogue evaluation datasets, among which two are from past DSTC series, and benchmark against state-of-the-art baselines. D-score not only outperforms the best baseline by a large margin in terms of system-level Spearman correlation but also represents an important step towards explainable dialogue scoring.