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A Personal Conversation Assistant Based on Seq2seq with Word2vec Cognitive Map

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A Personal Conversation Assistant Based on Seq2seq with Word2vec Cognitive Map A Personal Conversation Assistant Based on Seq2seq with Word2vec Cognitive Map 著者 Shen Maoyuan 出版者 法政大学大学院情報科学研究科 journal or 法政大学大学院紀要. 情報科学研究科編 publication title volume 14 page range 1-6 year 2019-03-31 URL http://doi.org/10.15002/00021920 A Personal Conversation Assistant Based on Seq2seq with Word2vec Cognitive Map Maoyuan Shen Graduate School of Computer and Information Sciences Hosei University Tokyo, Japan [email protected] Abstract—WeChat is one of social network applications that a kind of SMT. It has a single neural network of encoder- connects people widely. Huge data is generated when users decoder to map source sequence to target sequence, and conduct conversations, which can be used to enhance their lives. the whole neural network is jointly trained to maximize the This paper will describe how this data is collected, how to develop a personalized chatbot using personal conversation records. Our conditional probability of the source sequence generating the system will have a cognitive map based on the word2vec model, target sequence [4-7]. which is used to learn and store the relationship of each word that Sequence-to-sequence [5] with attention mechanism [6] appears in the chatting records. Each word will be mapped to is the most recently adopted architecture for conversational a continuous high dimensional vector space. Then the sequence- models [2, 7, 9]. It can capture the semantic and syntactic to-sequence framework (seq2seq) will be adopted to learn the chatting styles from all pairs of chatting sentences. Meanwhile, features between messages and responses in an end-to-end the traditional one-hot embedding layer will be replaced with our way [7]. Although this kind of model can generate some word2vec embedding layer in the seq2seq model. Furthermore, kind of reasonable responses to different types of questions an autoencoder of seq2seq architecture is trained to learn the and even extract knowledge from the training corpus, it has vector representation of each sentence, then the cosine similarity some obvious drawbacks [9]. One is the lack of a coherent between model generated response and the pre-existing response in test set can be evaluated , and the distance with principal personality, and another is the lack of general world knowledge component analysis (PCA) projection can be also displayed. As and flexibility of using it. Therefore, I propose improvements a result, our word2vec embedded seq2seq model significantly on these two aspects. outperforms the one-hot embedded one. The reason for the lack of coherent personality is that the Key Words—WeChat; Word2vec; Seq2seq; Attention Mecha- training data from the open social conversations disturbed its nism; Autoencoder character, because these conversations come from different participants. So I employ personal chat data decrypted from I. INTRODUCTION WeChat database and an open source single role response Due to people’s busy lifestyles nowadays, a chat assistant corpus1 in model training. who can accurately predict the user’s reply intention and chat Although seq2seq is a kind of semi-supervised learning, on behalf of them will be greatly welcomed. This kind of in fact it lacks flexibility in the mapping of sequences to desired AI can have the potential to be not just a chat pet, sequences. When we look closely at the implementation details but also a virtual self that grows up with you in the parallel of seq2seq, we can see that it uses one-hot in word embedding. space. Although human-machine conversational AI has a long While one-hot embedding can just carry the frequency identity way to go through Turing tests [1], research in this field has of a word rather than the information revealing its meaning. always been pursued by researchers. Such a sequence to sequence is just a monotonous match. When we consider about Short-Text Conversation (STC), Since Google open source word2vec, it has quickly become conventional methods can be classified into two categories [2]: a topic of general interest in the industry, because it’s fast and Information Retrieval (IR) based method and the Statistical efficient [10]. Machine Translation (SMT) based method. It has been shown Actually, word2vec is a concise language model [11], which that when a large corpus of status-response pairs is used in uses one layer neural network to map the one-hot form of word both methods, the SMT performance better than the IR [3]. vector to the distributed form of the word vector. In order Neural machine translation (NMT) is a new approach which to accelerate the training, it uses some techniques like the is widely used in machine translation [4-6]. Meanwhile, it’s hierarchical softmax, negative sampling, etc. [12] Word2vec also a very popular approach to build conversational models representation has a number of excellent features. First of all, [2, 7-9]. With large scale of public social conversation data, the word2vec representations of words carry the syntactic and those models are totally data-driven (i.e., without any manual semantic information [11], which makes the words of similar rules) to generate plausible responses. Essentially, the NMT is 1 https://github.com/fateleak/dgk lost conv/blob/master/results/ Supervisor: Prof. Runhe Huang xiaohuangji50w nofenci.conv.zip meaning can be projected to a close distance in the word2vec space. This feature is much like a cognitive map of language built by humans through learning. On the other hand, it can compress the dimensionality of one-hot vector representation, which will be a very good feature for seq2seq model, because that will reduce the input units of the RNN. Due to its good performance, word2vec has been applied to comments sentiment classification [13], named entity recognition [14], and the visually grounded word embeddings (vis-w2v) to capture visual notions of semantic relatedness [15]. II. RELATED WORK Our proposed model is based on word2vec [11], seq2seq [5] and attention mechanism [6], so let’s have a brief review of them. A. Word2vec Before the advent of word2vec, DNN has already been used to train word vector which is used to deal with the relationship between words and words. That method is a three-layer neural network structure (which can also be multilayered), which Fig. 1. Structure of word2vec (CBOW). can be divided into input layer, hidden layer and output layer (softmax layer) [16]. Word2vec is usually divided into CBOW (Continuous Bag- statement, and yt represents the current output word. The goal of-Words) and Skip-gram models depending on how it defines is described by fomula (1): the input and output of data. The training input of the CBOW model is the corresponding ny Y word vectors for the context of a particular word, and the p(yjx) = p(ytjy1; y2; : : : ; yt−1; x) (1) output is the word vector of this particular word. As the t=1 example shown in Fig. 1, our context values of 4 size, and this particular word is Learning, that is the term vector what As shown in fomula (2), our training objective is to mini- we need to output. There are 8 words in the context, 4 in mize the negative log-likelihood of the conditional probability front and back, and these 8 words are input to the model. of the target y given source x: Since CBOW uses the word bag model, these eight words are X all equal, that means the distance between them and the word J = − log(p(yjx)) (2) we care about is not considered, as long as it’s within our x;y2D context. The idea of the Skip-Gram model is reversed to CBOW, that Where D is our training set. means the input is the word vector of a particular word, and It’s easy to model the distribution of the conditional prob- the output is the context vectors of the words corresponding to ability p(yjx) with seq2seq. The model includes encoder and the particular word. For the example above, the context size is decoder. First, in the encoder section, the input is transmitted 4, the specific word Learning is the input, and these 8 words to the encoder, and then the hidden state C of the last time step before and after are the output. t is obtained, which is the basic function of Long Short-Term Word2vec uses CBOW and Skip-gram to train the model Memory (LSTM) cell. The C can be regarded as the context and get the word vector. It proposes an optimization method vector of the source sentence. Second, the context vector C to replace the hidden layer and the neuron in the output layer from the encoder is entered as an initial state to the LSTM with the data structure of the Huffman tree. The leaf node of the decoder. Then the decoder will calculate the output of the Huffman tree acts as the output layer neurons, and the sequence, and the output from the previous moment yt−1 will number of leaf nodes is the size of the vocabulary. The inner be the input for this moment to calculate yt. node acts as a hidden layer of neurons. In seq2seq model, the closer to the first cell of the decoder, the more influence it has on decoder, but it is not reasonable. B. Seq2seq and Attention Mechanism The attention mechanism was then proposed [6], and for each This structure of seq2seq is also called the encoder-decoder cell in the output, the importance of each word in the input mode [5]. We use x = fx1; x2; : : : ; xnx g represents the sequence will be detected. For each yt in the output y, it’s input statement, y = fy1; y2; : : : ; yny g represents the output affected by a context vector ct.
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