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Modeling Topical Relevance for Multi-Turn Dialogue Generation

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Modeling Topical Relevance for Multi-Turn Dialogue Generation Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20) Modeling Topical Relevance for Multi-Turn Dialogue Generation Hainan Zhang2∗ , Yanyan Lan14y , Liang Pang14 , Hongshen Chen2 , Zhuoye Ding2 and Dawei Yin4 1CAS Key Lab of Network Data Science and Technology, Institute of Computing Technology, CAS 2JD.com, Beijing, China 3Baidu.com, Beijing, China 4University of Chinese Academy of Sciences fzhanghainan6,chenhongshen,[email protected], flanyanyan,[email protected],[email protected] Abstract context1 `}，(吗？(Hello) context2 有什么问题我可以.¨b? (What can I do for you?) FÁM价了，我要3请保价 Topic drift is a common phenomenon in multi-turn context3 (The product price has dropped. I want a low-price.) dialogue. Therefore, an ideal dialogue generation }的，这边.¨3请，FÁ已Ï60了'？ context4 models should be able to capture the topic informa- (Ok, I will apply for you. Have you received the product?) 东西60了发h不(一w吗？ tion of each context, detect the relevant context, and context5 (I have received the product without the invoice together.) produce appropriate responses accordingly. How- 开w5P发h不会随'Äú context6 ever, existing models usually use word or sentence (The electronic invoice will not be shipped with the goods.) level similarities to detect the relevant contexts, current context /发我®±吗？(Is it sent to my email？) /的，请¨Ð供®±0@，5P发h24小时Äú。 which fail to well capture the topical level rele- response (Yes, please provide your email address， vance. In this paper, we propose a new model, we will send the electronic invoices in 24 hours.) named STAR-BTM, to tackle this problem. Firstly, the Biterm Topic Model is pre-trained on the whole Table 1: The example from the customer services dataset. The word training dataset. Then, the topic level attention color indicates the relevant topic word in the contexts and response, weights are computed based on the topic repre- showing the topic-drift phenomenon. sentation of each context. Finally, the attention weights and the topic distribution are utilized in the decoding process to generate the corresponding re- neural network (RNN) based encoder is first used to encode sponses. Experimental results on both Chinese cus- each context as a sentence-level vector, and then a hierarchi- tomer services data and English Ubuntu dialogue cal RNN is utilized to encode these context vectors to a his- data show that STAR-BTM significantly outper- tory representation. In the decoding process, another RNN forms several state-of-the-art methods, in terms of decoder is conducted to generate the response based on the both metric-based and human evaluations. history representation. The parameters of both encoder and decoder are learned by maximizing the averaged likelihood of the training data. However, the desired response is usu- 1 Introduction ally only dependent on some relevant contexts, instead of all the contexts. Recently, some works have been proposed to Multi-turn dialogue generation is widely used in many natu- model the relevant contexts by using some similarity mea- ral language processing (NLP) applications, such as customer sures. For example, Tian et al. [2017] calculates the co- services, mobile assistant and chatbots. Given a conversa- sine similarity of the sentence embedding between the cur- tion history containing several contexts, a dialogue genera- rent context and the history contexts as the attention weights, tion model is required to automatically output an appropri- Xing et al. [2018] introduces the word and sentence level at- ate response. Therefore, how to fully understand and utilize tention mechanisms to HRED, and Zhang et al. [2019] uti- these contexts is important for designing a good multi-turn lizes the sentences level self-attention mechanism to detect dialogue generation model. the relevant contexts. However, these similarities are defined Different from single-turn dialogue generation, people usu- on either word or sentence level, which cannot well tackle the ally model the multi-turn dialogue generation in a hierar- topic drift problem in multi-turn dialogue generation. chical way. A typical example is the Hierarchical Recur- rent Encoder-Decoder (HRED) model [Serban et al., 2016; Here we give an example conversation, as shown in Ta- Sordoni et al., 2015]. In the encoding phase, a recurrent ble 1. The contexts are of three different topics. The (context1,context2) pair talks about ‘greeting’, the (con- ∗This work was done when the first author was a Ph.D student at text3,context4) pair talks about ‘low-price’, and the (con- ICT, CAS. text5,...,response) pair talks about ‘invoice’. In this case, us- yCorresponding Author. ing all the contexts indiscriminately will obviously introduce 3737 Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20) many noises to the decoding process, which will hurt the per- criminately for the multi-turn dialogue generation task, since formance of the multi-turn dialogue generation model. If we the responses are usually only associated with a portion of the use word level similarities to locate the relevant contexts, the previous contexts. Therefore, some researchers try to use the current context and context4 in the example will be associ- similarity measure to define the relevance of the context. Tian ated because ‘send’ and ‘receive’ are highly similar words, et al. [Tian et al., 2017] propose a weighted sequence (WSeq) which is clearly wrong. If we use sentence level similarities attention model for HRED, which uses the cosine similarity to locate the relevant contexts, it may still involve the false as the attention weight to measure the correlation of the con- relevant context4 into consideration. texts. But this model only uses the unsupervised sentence We argue that context relevance should be computed at the level representation, which fails to capture some detailed se- topic level, to better tackle the topic drift problem in multi- mantic information. Recently, Xing et al. [Xing et al., 2018] turn dialogue generation. From both linguistic and cogni- introduced the traditional attention mechanism [Bahdanau et tive perspective, topic is the high level cluster of knowledge, al., 2015] into HRED, named hierarchical recurrent attention which can describe the relationship of sentences in the con- network (HRAN). In this model, the weight of attention is text, and has an important role in human dialogue for direct- calculated based on the current state, the sentence level rep- ing focus of attention. In this paper, we propose a new model, resentation and the word level representation. However, the namely STAR-BTM, to model the Short-text Topic-level At- word level attention may introduce some noisy relevant con- tention Relevance with Biterm Topic Model (BTM) [Yan et texts. Shen et al. [Chen et al., 2018] propose to introduce the al., 2013]. Specifically, we first pre-train the BTM model memory network into the VHRED model, so that the model on the whole training data, which split every customer-server can remember the context information. Theoretically, it can pair in the context as a short document. Then, we use the retrieve some relevant information from the memory in the BTM to get each sentence topic distribution and calculate decoding phase, however, it is not clearly whether and how the topic distribution similarity between the current context the system accurately extracts the relevant contexts. Zhang and each history context as the relevance attention. Finally, et al. [Zhang et al., 2019] proposed to use the sentence level we utilize the relevance attention and the topic distribution to self-attention to model the long distance dependency of con- conduct the decoding process. The BTM model and the text texts, to detect the relevant context for the multi-turn dialogue generation model are jointly learned to improve their perfor- generation. Though it has the ability to tackle the position mances in this process. bias problem, the sentence level self-attention is still limited In our experiments, we use two public datasets to evalu- in capturing the topic level relevant contexts. ate our proposed models, i.e., Chinese customer services and The motivation of this paper is to detect the topic level at- English Ubuntu dialogue corpus. The experimental results tention relevance for multi-turn dialogue generation. It is a show that STAR-BTM generates more informative and suit- more proper way to deal with the topic draft problem, as com- able responses than traditional HRED models and its atten- pared with the traditional word or sentence level methods. tion variants, in terms of both metric-based evaluation and Some previous works[Xing et al., 2017; Xing et al., 2018] human evaluation. Besides, we have shown the relevant at- have been proposed to use topic model in dialogue genera- tention words, indicating that STAR-BTM obtains coherent tion. They maily use the topic model to provide some topic results with human’s understanding. related words for generation, while our work focuses on de- tecting the topic level relevant contexts. 2 Related Work 3 STAR-BTM Recently, multi-turn dialogue generation has gained more at- tention in both research community and industry, compared In this section, we will describe our Short-text Topic Atten- with the single-turn dialogue generation [Li et al., 2017; tion Relevance with Biterm Topic Model (STAR-BTM) in de- Mou et al., 2017; Zhang et al., 2018a; Zhang et al., 2018b]. tail, with the architecture shown in Figure 1. STAR-BTM One of the reasons is that it is closely related to the real consists of three modules, i.e. , the pre-trained BTM model, application, such as chatbot and customer service. More topic level attention module and the joint learning decoder. importantly, multi-turn dialogue generation needs to con- Firstly, we pre-train the BTM model on the whole training sider more information and constraints [Chen et al., 2018; data, to obtain the topic word distribution of each context.
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