Dial2vec: Self-Guided Contrastive Learning of Unsupervised Dialogue Embeddings (2210.15332v1)

Abstract: In this paper, we introduce the task of learning unsupervised dialogue embeddings. Trivial approaches such as combining pre-trained word or sentence embeddings and encoding through pre-trained LLMs (PLMs) have been shown to be feasible for this task. However, these approaches typically ignore the conversational interactions between interlocutors, resulting in poor performance. To address this issue, we proposed a self-guided contrastive learning approach named dial2vec. Dial2vec considers a dialogue as an information exchange process. It captures the conversational interaction patterns between interlocutors and leverages them to guide the learning of the embeddings corresponding to each interlocutor. The dialogue embedding is obtained by an aggregation of the embeddings from all interlocutors. To verify our approach, we establish a comprehensive benchmark consisting of six widely-used dialogue datasets. We consider three evaluation tasks: domain categorization, semantic relatedness, and dialogue retrieval. Dial2vec achieves on average 8.7, 9.0, and 13.8 points absolute improvements in terms of purity, Spearman's correlation, and mean average precision (MAP) over the strongest baseline on the three tasks respectively. Further analysis shows that dial2vec obtains informative and discriminative embeddings for both interlocutors under the guidance of the conversational interactions and achieves the best performance when aggregating them through the interlocutor-level pooling strategy. All codes and data are publicly available at https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/dial2vec.

Essay on "Dial2vec: Self-Guided Contrastive Learning of Unsupervised Dialogue Embeddings"

The discussed paper presents an examination into the task of generating unsupervised dialogue embeddings, a critical element in understanding conversational semantics. Traditional methodologies have depended on combining pre-trained word or sentence embeddings, as well as encoding via pre-trained LLMs (PLMs). These approaches, however, tend to sidestep the essential conversational interactions between interlocutors, which leads to suboptimal performance. The paper introduces a novel approach dubbed "dial2vec," which aims to bridge this performance gap by capturing dialogue as an interaction-driven process.

Core Contribution

Dial2vec is characterized by a focus on self-guided contrastive learning, where the dialogue embedding process is informed by the dynamics of information exchange among interlocutors. Specifically, dial2vec models the conversation to generate embeddings for each participant by analyzing their interaction patterns, subsequently aggregating these embeddings for a comprehensive representation. This methodology prioritizes the interactions over individual utterances, identifying them as crucial to capturing the dialogue's semantic essence.

Experimental Validation and Results

To substantiate their proposal, the researchers implemented dial2vec on a robust benchmark consisting of six prominent dialogue datasets: BiTOD, Doc2dial, MetalWOZ, MultiWOZ, Self-dialogue, and SGD. They evaluated performance across three tasks: domain categorization, semantic relatedness, and dialogue retrieval. Empirical results illustrate that dial2vec elevates the performance standards significantly compared to existing baselines—showcasing average improvements of 8.7 in purity, 9.0 in Spearman's correlation, and a notable 13.8 in mean average precision (MAP) across these tasks.

Detailed Analysis

Further investigations into dial2vec highlight its capacity to generate both informative and discriminative embeddings. The strategy of leveraging conversational interactions allows the model to enhance self-representations for each interlocutor while simultaneously mitigating extraneous information. Additionally, the proposed interlocutor-level pooling strategy for aggregation is demonstrated to be particularly effective, achieving superior results compared to simpler averaging methods.

Implications and Future Directions

The exploration into embeddings that encapsulate interaction dynamics signifies a notable advancement for dialogue-based applications, including dialogue clustering and conversational sentiment analysis. The dial2vec approach holds promise for refining technologies in areas that require nuanced understanding of dialogues, such as context-aware AI and sophisticated conversational agents.

Nevertheless, the paper acknowledges limitations in scaling dial2vec to multi-party dialogues due to dataset constraints. Future advancements could focus on creating suitable datasets for evaluating multi-party dialogue systems and refining dial2vec’s adaptability across different input embeddings, particularly those based on BERT-like architectures, which currently exhibit training inconsistencies.

In conclusion, the paper provides a comprehensive framework for understanding and addressing the challenges of unsupervised dialogue embeddings through the innovative dial2vec model. The strong numerical results across a diverse array of datasets emphasize its potential impact, setting a precedent for future research in improving dialogue understanding within AI systems.