Improving Knowledge-aware Dialogue Generation via Knowledge Base Question Answering (1912.07491v1)

Abstract: Neural network models usually suffer from the challenge of incorporating commonsense knowledge into the open-domain dialogue systems. In this paper, we propose a novel knowledge-aware dialogue generation model (called TransDG), which transfers question representation and knowledge matching abilities from knowledge base question answering (KBQA) task to facilitate the utterance understanding and factual knowledge selection for dialogue generation. In addition, we propose a response guiding attention and a multi-step decoding strategy to steer our model to focus on relevant features for response generation. Experiments on two benchmark datasets demonstrate that our model has robust superiority over compared methods in generating informative and fluent dialogues. Our code is available at https://github.com/siat-nlp/TransDG.

Enhancing Dialogue Systems through KBQA-Based Knowledge Integration

The paper "Improving Knowledge-aware Dialogue Generation via Knowledge Base Question Answering" addresses the integration of commonsense knowledge in open-domain dialogue systems, a critical aspect of developing more informative and factually enriched conversational agents. The authors propose TransDG, a novel system that leverages techniques from Knowledge Base Question Answering (KBQA) to enhance dialogue generation capabilities by transferring question representation and knowledge matching attributes.

Core Methodology

TransDG operates by incorporating a pre-trained KBQA model to guide it in factual knowledge selection and utterance comprehension. The model comprises two main components: the encoding layer from KBQA, which provides question-level insights, and a multi-step decoding process that refines knowledge selection for response generation. The decoding process includes a two-step mechanism where the first-step generates a draft response using entity attention vectors extracted from a commonsense knowledge base, and the second-step refines the draft by attending additional context learned earlier, thus producing a final response that is coherent and entity-relevant.

A key innovation is the response guiding attention mechanism which selects top-k candidate responses from a retrieval component, thereby improving context representation. This approach addresses traditional shortcomings in dialogue systems, namely the difficulty in matching posts to relevant knowledge base entries due to vague subject-relational queries and improving response informativeness and coherence without overwhelming reliance on insufficient input data.

Results and Implications

On extensive experimentation using benchmark datasets, including the SimpleQuestions and Reddit dialogue datasets, TransDG demonstrated superior performance in generating more informative and contextually accurate dialogues compared to existing methodologies like Seq2Seq, CopyNet, and others. The results showed lower perplexity and more favorable entity scores. Additionally, the multi-step decoding approach retained relevance and appropriateness, affirming the model's ability to balance factual infusion with fluent conversational flow. Quantitative metrics such as BLEU scores further corroborate the system's enhanced response quality.

Future Directions

The authors articulate several promising pathways for future research, including optimizing entity linking within dialogues to enhance knowledge precision, expanding the knowledge base to include broader domains, and iteratively refining the retrieval response attention mechanism to accommodate evolving contextual requirements. The impact on AI development lies in its potential to set a new standard for dialogue systems through informed conversation growth, reducing instances of nonsensical or repetitive responses prevalent in current generations.

TransDG's integrative approach paves the way for dialogue systems that can reliably mimic human-like interactions not merely through structural comprehension but also through thematic and factual engagement, making it a valuable addition to the AI dialogue generation toolkit.