Recent advances in sequence-to-sequence learning reveal a purely data-driven approach to the response generation task. Despite its diverse applications, existing neural models are prone to producing short and generic replies, making it infeasible to tackle open-domain challenges. In this research, we analyze this critical issue in light of the model's optimization goal and the specific characteristics of the human-to-human dialog corpus. By decomposing the black box into parts, a detailed analysis of the probability limit was conducted to reveal the reason behind these universal replies. Based on these analyses, we propose a max-margin ranking regularization term to avoid the models leaning to these replies. Finally, empirical experiments on case studies and benchmarks with several metrics validate this approach.
To address this issue, various works are conducted on bringing more information to influence these models. propose to utilize keywords to constrain the topic of responses, and incorporates the replies with topic information. By contrary, some researcher believed in diverse responses are just buried by the greedy beam-search rules @cite_7 , so they focus on involving more punishment or randomness in the inference stages. To illustrate, constrain the search space using mutual information with the query and randomly chose candidate words from top beams to consist short phrases. These existing works mainly focus on the generation strategies during inference, in contrast, the model's architecture and loss function have rarely been explored.
@cite_7
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Search strategies for generating a response from a neural dialogue model have received relatively little attention compared to improving network architectures and learning algorithms in recent years. In this paper, we consider a standard neural dialogue model based on recurrent networks with an attention mechanism, and focus on evaluating the impact of the search strategy. We compare four search strategies: greedy search, beam search, iterative beam search and iterative beam search followed by selection scoring. We evaluate these strategies using human evaluation of full conversations and compare them using automatic metrics including log-probabilities, scores and diversity metrics. We observe a significant gap between greedy search and the proposed iterative beam search augmented with selection scoring, demonstrating the importance of the search algorithm in neural dialogue generation.