Attention mechanism has become a popular and widely used component in sequence-to-sequence models. However, previous research on neural generative dialogue systems always generates universal responses, and the attention distribution learned by the model always attends to the same semantic aspect. To solve this problem, in this paper, we propose a novel Multi-Head Attention Mechanism (MHAM) for generative dialog systems, which aims at capturing multiple semantic aspects from the user utterance. Further, a regularizer is formulated to force different attention heads to concentrate on certain aspects. The proposed mechanism leads to more informative, diverse, and relevant response generated. Experimental results show that our proposed model outperforms several strong baselines.
Text style transfer task requires the model to transfer a sentence of one style to another style while retaining its original content meaning, which is a challenging problem that has long suffered from the shortage of parallel data. In this paper, we first propose a semi-supervised text style transfer model that combines the small-scale parallel data with the large-scale nonparallel data. With these two types of training data, we introduce a projection function between the latent space of different styles and design two constraints to train it. We also introduce two other simple but effective semisupervised methods to compare with. To evaluate the performance of the proposed methods, we build and release a novel style transfer dataset that alters sentences between the style of ancient Chinese poem and the modern Chinese.
The availability of abundant conversational data on the Internet brought prosperity to the generation-based open domain conversation systems. In the training of the generation models, existing methods generally treat all the training data equivalently. However, the data crawled from the websites may contain many noises. Blindly training with the noisy data could harm the performance of the final generation model. In this paper, we propose a generation with calibration framework, that allows high- quality data to have more influences on the generation model and reduces the effect of noisy data. Specifically, for each instance in training set, we employ a calibration network to produce a quality score for it, then the score is used for the weighted update of the generation model parameters. Experiments show that the calibrated model outperforms baseline methods on both automatic evaluation metrics and human annotations.
Previous research on dialogue systems generally focuses on the conversation between two participants, yet multi-party conversations which involve more than two participants within one session bring up a more complicated but realistic scenario. In real multiparty conversations, we can observe who is speaking, but the addressee information is not always explicit. In this paper, we aim to tackle the challenge of identifying all the missing addressees in a conversation session. To this end, we introduce a novel who-to-whom (W2W) model which models users and utterances in the session jointly in an interactive way. We conduct experiments on the benchmark Ubuntu Multi-Party Conversation Corpus and the experimental results demonstrate that our model outperforms baselines with consistent improvements.
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