Augmenting Neural Response Generation with Context-Aware Topical Attention (1811.01063v2)

Abstract: Sequence-to-Sequence (Seq2Seq) models have witnessed a notable success in generating natural conversational exchanges. Notwithstanding the syntactically well-formed responses generated by these neural network models, they are prone to be acontextual, short and generic. In this work, we introduce a Topical Hierarchical Recurrent Encoder Decoder (THRED), a novel, fully data-driven, multi-turn response generation system intended to produce contextual and topic-aware responses. Our model is built upon the basic Seq2Seq model by augmenting it with a hierarchical joint attention mechanism that incorporates topical concepts and previous interactions into the response generation. To train our model, we provide a clean and high-quality conversational dataset mined from Reddit comments. We evaluate THRED on two novel automated metrics, dubbed Semantic Similarity and Response Echo Index, as well as with human evaluation. Our experiments demonstrate that the proposed model is able to generate more diverse and contextually relevant responses compared to the strong baselines.

Augmenting Neural Response Generation with Context-Aware Topical Attention

The paper "Augmenting Neural Response Generation with Context-Aware Topical Attention" presents an advanced sequence-to-sequence (Seq2Seq) architecture specifically designed to enhance the quality of multi-turn conversation responses. This novel architecture, termed the Topical Hierarchical Recurrent Encoder Decoder (THRED), seeks to address the common limitations inherent in traditional Seq2Seq models, which often result in generic and contextually weak responses.

Model Architecture and Innovations

THRED builds upon the basic Seq2Seq framework by integrating a hierarchical joint attention mechanism that considers both conversation history and topical concepts. This is achieved by employing a two-tier attention mechanism—context attention and topic attention—where the former focuses on salient parts of the conversation history, and the latter incorporates relevant topical words derived from a Latent Dirichlet Allocation (LDA) model. The introduction of these attention layers signals a shift from word-frequency-centric response generation to more substantive, contextually aware modeling.

Datasets and Evaluation Metrics

A significant contribution of this paper is the development of a cleaned conversational dataset obtained from Reddit comments. This dataset is instrumental in training and evaluating the efficacy of the THRED model. Furthermore, the paper proposes two novel automated metrics: the Semantic Similarity (SS) and Response Echo Index (REI). The SS metric assesses the model's ability to produce responses consistent with conversation context, while the REI quantifies overfitting by measuring the novelty of generated responses compared to training data. The SS metric specifically demonstrated a correlation with human judgment, validating its suitability for automatic evaluation of dialogue systems.

Experimental Results

Extensive experiments reveal that THRED consistently generates more diverse and contextually relevant responses than traditional baselines such as Seq2Seq, HRED, and TA-Seq2Seq. These improvements are quantitatively supported by superior performance on the SS and REI metrics across multiple datasets, with THRED also achieving favorable ratings in human evaluations. The model's capability to remain on topic and produce engaging responses highlights the importance of leveraging both conversation history and external topic information in response generation.

Implications and Future Work

The implications of this research are manifold. Practically, THRED pushes the boundaries of what is achievable in data-driven dialogue systems, providing tangible techniques for the development of more engaging conversational agents. Theoretically, the introduction of context-aware topical attention mechanisms offers a promising direction for research into context-sensitive neural networks. Future developments could explore the integration of more advanced topic models, broader datasets, or reinforcement learning techniques to further enhance dialogue systems' performance.

The introduction of new evaluation metrics is a critical advancement, facilitating the rapid testing of dialogue systems without relying solely on human evaluation. Continued exploration into automated evaluation methods will be vital in scaling the development and assessment of conversational AI. Overall, this paper makes a substantial contribution to the field of conversational AI by presenting a sophisticated model that enhances both the quality and engagement of generated dialogue exchanges.