Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence Models (1701.03185v2)

Abstract: Sequence-to-sequence models have been applied to the conversation response generation problem where the source sequence is the conversation history and the target sequence is the response. Unlike translation, conversation responding is inherently creative. The generation of long, informative, coherent, and diverse responses remains a hard task. In this work, we focus on the single turn setting. We add self-attention to the decoder to maintain coherence in longer responses, and we propose a practical approach, called the glimpse-model, for scaling to large datasets. We introduce a stochastic beam-search algorithm with segment-by-segment reranking which lets us inject diversity earlier in the generation process. We trained on a combined data set of over 2.3B conversation messages mined from the web. In human evaluation studies, our method produces longer responses overall, with a higher proportion rated as acceptable and excellent as length increases, compared to baseline sequence-to-sequence models with explicit length-promotion. A back-off strategy produces better responses overall, in the full spectrum of lengths.

Analyzing "Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence Models"

The paper "Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence Models" by Louis Shao and colleagues tackles the intricate task of enhancing conversation response generation via sequence-to-sequence (seq2seq) models. Distinguished from translation tasks, conversation response generation necessitates creativity, requiring outputs that are not only coherent and informative but also diverse and engaging. This paper focuses on the single-turn setting and introduces innovative methods to improve the length, coherence, and diversity of the responses generated by seq2seq models.

Key Contributions

1. Incorporation of Self-Attention in Decoders: To address the challenges of generating longer responses, the authors introduce self-attention within the decoder. This adaptation aids in maintaining coherence by better managing the contextual flow of lengthy outputs, effectively increasing the model's ability to produce relevant and sustained interactions.
2. Introduction of the Glimpse Model: The paper proposes a practical system, termed the 'glimpse model', that processes fixed-length segments of the target sequence. This method enables the scaling of training processes across large datasets without encountering memory constraints typically associated with target-side attention in traditional models.
3. Stochastic Beam-Search with Segment-by-Segment Reranking: The authors adapt beam search by incorporating a stochastic element and segment-by-segment reranking, thereby enhancing diversity. Instead of relying on a complete sequence reranking, this approach injects variation early in the generation process, thus improving the overall diversity of responses.

Experimental Evaluation and Results

The paper conducted extensive training using over 2.3 billion conversation messages culled from various web sources. The experimental setup assessed model outputs using both automated metrics and human evaluations. Notably, the proposed methods yielded longer responses that received a higher percentage of acceptable and excellent ratings in human evaluations. Compared to baseline seq2seq models that employed explicit length-promotion heuristics, the authors demonstrate that their approach produces superior quality responses over a broad range of lengths. Furthermore, the use of a fallback strategy improved response quality across different lengths, bolstering the model's utility for real-world applications.

Implications and Future Directions

The contributions of this paper underscore a significant stride in conversation modeling, particularly in generating longer and more engaging responses. Notably, the proposed techniques hold potential for application in various sequence-to-sequence domains, such as machine translation and image captioning, due to their ability to enhance coherence and diversity. Moreover, the findings suggest pathways for further refinement in neural architectures and data-driven approaches, potentially enhancing conversational agents' capabilities to pass the Turing Test.

Future research could explore hybrid models that integrate contextual and multi-turn settings, thereby advancing conversational AI to offer more nuanced and contextually relevant interactions. Additionally, further experimentation with different decoding strategies and hybrid attention mechanisms may unleash new potentials in generating human-like responses across varied conversational scenarios.

In sum, this paper presents a comprehensive approach to overcoming prevalent challenges in conversation response generation, making notable strides in advancing sequence-to-sequence modeling capabilities for AI-assisted communications.