text: 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.
  @cite_7: Sequence-to-sequence neural translation models learn semantic and syntactic relations between sentence pairs by optimizing the likelihood of the target given the source, i.e., @math , an objective that ignores other potentially useful sources of information. We introduce an alternative objective function for neural MT that maximizes the mutual information between the source and target sentences, modeling the bi-directional dependency of sources and targets. We implement the model with a simple re-ranking method, and also introduce a decoding algorithm that increases diversity in the N-best list produced by the first pass. Applied to the WMT German English and French English tasks, the proposed models offers a consistent performance boost on both standard LSTM and attention-based neural MT architectures.
summary: 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.