We study response selection for multi-turn conversation in retrieval-based chatbots. Existing work either concatenates utterances in context or matches a response with a highly abstract context vector finally, which may lose relationships among utterances or important contextual information. We propose a sequential matching network (SMN) to address both problems. SMN first matches a response with each utterance in the context on multiple levels of granularity, and distills important matching information from each pair as a vector with convolution and pooling operations. The vectors are then accumulated in a chronological order through a recurrent neural network (RNN) which models relationships among utterances. The final matching score is calculated with the hidden states of the RNN. An empirical study on two public data sets shows that SMN can significantly outperform state-of-the-art methods for response selection in multi-turn conversation.
Recently, building a chatbot with data driven approaches @cite_3 @cite_1 has drawn significant attention. Existing work along this line includes retrieval-based methods @cite_28 @cite_1 @cite_23 @cite_16 @cite_17 @cite_9 @cite_19 and generation-based methods @cite_2 @cite_6 @cite_12 @cite_26 @cite_24 @cite_8 @cite_4 . Our work is a retrieval-based method, in which we study context-based response selection. Early studies of retrieval-based chatbots focus on response selection for single-turn conversation @cite_27 @cite_1 @cite_23 @cite_17 . Recently, researchers have begun to pay attention to multi-turn conversation. For example, match a response with the literal concatenation of context utterances. concatenate context utterances with the input message as reformulated queries and perform matching with a deep neural network architecture. improve multi-turn response selection with a multi-view model including an utterance view and a word view. Our model is different in that it matches a response with each utterance at first and accumulates matching information instead of sentences by a GRU, thus useful information for matching can be sufficiently retained.
@cite_26 @cite_4 @cite_8 @cite_28 @cite_9 @cite_1 @cite_3 @cite_6 @cite_24 @cite_19 @cite_27 @cite_23 @cite_2 @cite_16 @cite_12 @cite_17
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We study response selection for multi-turn conversation in retrieval-based chatbots. Existing work either concatenates utterances in context or matches a response with a highly abstract context vector finally, which may lose relationships among utterances or important contextual information. We propose a sequential matching network (SMN) to address both problems. SMN first matches a response with each utterance in the context on multiple levels of granularity, and distills important matching information from each pair as a vector with convolution and pooling operations. The vectors are then accumulated in a chronological order through a recurrent neural network (RNN) which models relationships among utterances. The final matching score is calculated with the hidden states of the RNN. An empirical study on two public data sets shows that SMN can significantly outperform state-of-the-art methods for response selection in multi-turn conversation. 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. 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. This paper considers the task of matching images and sentences. The challenge consists in discriminatively embedding the two modalities onto a shared visual-textual space. Existing work in this field largely uses Recurrent Neural Networks (RNN) for text feature learning and employs off-the-shelf Convolutional Neural Networks (CNN) for image feature extraction. Our system, in comparison, differs in two key aspects. Firstly, we build a convolutional network amenable for fine-tuning the visual and textual representations, where the entire network only contains four components, i.e., convolution layer, pooling layer, rectified linear unit function (ReLU), and batch normalisation. End-to-end learning allows the system to directly learn from the data and fully utilise the supervisions. Secondly, we propose instance loss according to viewing each multimodal data pair as a class. This works with a large margin objective to learn the inter-modal correspondence between images and their textual descriptions. Experiments on two generic retrieval datasets (Flickr30k and MSCOCO) demonstrate that our method yields competitive accuracy compared to state-of-the-art methods. Moreover, in language person retrieval, we improve the state of the art by a large margin. Code is available at this https URL com layumi Image-Text-Embedding