In this paper, we propose an interactive matching network (IMN) to enhance the representations of contexts and responses at both the word level and sentence level for the multi-turn response selection task. First, IMN constructs word representations from three aspects to address the challenge of out-of-vocabulary (OOV) words. Second, an attentive hierarchical recurrent encoder (AHRE), which is capable of encoding sentences hierarchically and generating more descriptive representations by aggregating with an attention mechanism, is designed. Finally, the bidirectional interactions between whole multi-turn contexts and response candidates are calculated to derive the matching information between them. Experiments on four public datasets show that IMN significantly outperforms the baseline models by large margins on all metrics, achieving new state-of-the-art performance and demonstrating compatibility across domains for multi-turn response selection.
Chatbots aim to engage users in human-computer conversations in the open domain and are currently receiving increasing attention because they can target unstructured dialogue without a priori logical representation of the information exchanged during the conversation. Existing work on building chatbots includes generation-based methods @cite_3 @cite_30 and retrieval-based methods @cite_4 @cite_35 @cite_36 @cite_17 @cite_31 @cite_21 . Generation-based models maximize the probability of generating a response given the previous dialogue. This approach enables the incorporation of rich context when mapping between consecutive dialogue turns. Retrieval-based chatbots have the advantage of informative and fluent responses because they select a proper response for the current conversation from a repository by means of response selection algorithms.
@cite_30 @cite_35 @cite_4 @cite_36 @cite_21 @cite_3 @cite_31 @cite_17
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Learning to chat is a fascinating machine learning task with many applications from user-modeling to artificial intelligence. However, most of the work to date relies on designing large hard-wired sets of rules. On the other hand, the growth of social networks on the web provides large quantities of conversational data, suggesting that the time is ripe to train chatbots in a more data driven way. A first step is to learn to chat by ranking the response repository to provide responses that are consistent with the user’s expectations. Here we use a three phase ranking approach for predicting suitable responses to a query in a conversation. Sentences are first filtered, then efficiently ranked, and then more precisely re-ranked in order to select the most suitable response. The filtering is done using part-of-speech tagging, hierarchical clustering, and entropy analysis methods. The first phase ranking is performed by generating a large set of high-level grammatical and conceptual features, exploiting dictionaries and similarity measurement resources such as wikipedia similarity graphs, and by ranking using a boosted regression tree (MART) classifier. The more precise (conceptual) ranking is performed by designing more conceptual features obtained from similarity measurement resources such as query refinement and suggestion systems, sentence paraphrasing techniques, LDA topic modeling and structural clustering, and entropy analysis over wikipedia similarity graphs. The sentences are then ranked according to the confidence of a Transfer AdaBoost classifier, trained using transfer-learning methods in which a classification over a large corpus of noisy twitter and live-journal data is considered as the source domain, and the collaborative ranking of actively collected conversations, which are labeled in an online framework using user feedback, is considered as the destination domain. We give results on the performance of each step, and on the accuracy of our three phase ranking framework. Open-domain human-computer conversation has attracted much attention in the field of NLP. Contrary to rule- or template-based domain-specific dialog systems, open-domain conversation usually requires data-driven approaches, which can be roughly divided into two categories: retrieval-based and generation-based systems. Retrieval systems search a user-issued utterance (called a query) in a large database, and return a reply that best matches the query. Generative approaches, typically based on recurrent neural networks (RNNs), can synthesize new replies, but they suffer from the problem of generating short, meaningless utterances. In this paper, we propose a novel ensemble of retrieval-based and generation-based dialog systems in the open domain. In our approach, the retrieved candidate, in addition to the original query, is fed to an RNN-based reply generator, so that the neural model is aware of more information. The generated reply is then fed back as a new candidate for post-reranking. Experimental results show that such ensemble outperforms each single part of it by a large margin. Recent neural models of dialogue generation offer great promise for generating responses for conversational agents, but tend to be shortsighted, predicting utterances one at a time while ignoring their influence on future outcomes. Modeling the future direction of a dialogue is crucial to generating coherent, interesting dialogues, a need which led traditional NLP models of dialogue to draw on reinforcement learning. In this paper, we show how to integrate these goals, applying deep reinforcement learning to model future reward in chatbot dialogue. The model simulates dialogues between two virtual agents, using policy gradient methods to reward sequences that display three useful conversational properties: informativity (non-repetitive turns), coherence, and ease of answering (related to forward-looking function). We evaluate our model on diversity, length as well as with human judges, showing that the proposed algorithm generates more interactive responses and manages to foster a more sustained conversation in dialogue simulation. This work marks a first step towards learning a neural conversational model based on the long-term success of dialogues. Task-oriented dialogue focuses on conversational agents that participate in user-initiated dialogues on domain-specific topics. In contrast to chatbots, which simply seek to sustain open-ended meaningful discourse, existing task-oriented agents usually explicitly model user intent and belief states. This paper examines bypassing such an explicit representation by depending on a latent neural embedding of state and learning selective attention to dialogue history together with copying to incorporate relevant prior context. We complement recent work by showing the effectiveness of simple sequence-to-sequence neural architectures with a copy mechanism. Our model outperforms more complex memory-augmented models by 7 in per-response generation and is on par with the current state-of-the-art on DSTC2.