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“Shall I Be Your Chat Companion?” Towards an Online Human-Computer Conversation System Rui Yan1;3 Yiping Song2 1Institute of Computer Science 2Department of Computer and Technology (ICST) Science and Technology Peking University Peking University Beijing 100871, China Beijing 100871, China [email protected] [email protected] Xiangyang Zhou3 Hua Wu3 3Baidu Inc. 3Baidu Inc. No. 10 Xibeiwang East Road, No. 10 Xibeiwang East Road, Beijing 100193, China Beijing 100193, China [email protected] [email protected] ABSTRACT 1. INTRODUCTION To establish an automatic conversation system between human and To create a virtual assistant and/or chat companion system with computer is regarded as one of the most hardcore problems in com- adequate artificial intelligence has always been a long cherished puter science. It requires interdisciplinary techniques in informa- goal for researchers and practitioners. It is believed to be chal- tion retrieval, natural language processing, and data management, lenging for computers to maintain a relevant, meaningful and con- etc. The challenges lie in how to respond like a human, and to tinuous conversation with humans. How to respond like a human maintain a relevant, meaningful, and continuous conversation. The generally involves interdisciplinary techniques such as information arrival of big data era reveals the feasibility to create such a system retrieval, natural language processing, as well as big data manage- empowered by data-driven approaches. We can now organize the ment. A significant amount of efforts have been devoted to the conversational data as a chat companion. In this paper, we intro- research for decades, and promising achievements have been grad- duce a chat companion system, which is a practical conversation ually achieved so that we can expect real applications in real life, system between human and computer as a real application. Giv- rather than in Sci-Fi movies or research labs only. en the human utterances as queries, our proposed system will re- The demand for virtual assistant/chat companion system lead- spond with corresponding replies retrieved and highly ranked from s to cutting-edge technology in the spotlight from both academia a massive conversational data repository. Note that “practical” here and industry. The arrival of big data era also accelerates the de- indicates effectiveness and efficiency: both issues are important for velopment of human-computer conversation studies. Owing to the a real-time system based on a massive data repository. We have public resources for conversations on the web, we are likely to learn two scenarios of single-turn and multi-turn conversations. In our what to reply given (almost) any inputs by retrieving from the con- system, we have a base ranking without conversational context in- versational repository. It is probably a great timing to build such formation (for single-turn) and a context-aware ranking (for multi- data-driven conversation systems between humans and computer- turn). Both rankings can be conducted either by a shallow learning s. We are motivated to establish an online chat companion system or deep learning paradigm. We combine these two rankings to- for real-time services. Since there is a clear industry-driven back- gether in optimization. In the experimental setups, we investigate ground for our study, we ought to make the system practical, where the performance between effectiveness and efficiency for the pro- “practical” means both effectiveness and efficiency. These two is- posed methods, and we also compare against a series of baselines sues are fundamental for an online system. to demonstrate the advantage of the proposed framework in terms Human-computer conversation systems have been evolving for of p@1, MAP, and nDCG. We present a new angle to launch a prac- years. Researchers have firstly investigated into task-oriented con- tical online conversation system between human and computer. versation systems [34, 40, 46]. To maintain conversations with- in a specified domain, it would be more feasible to create prior knowledge to handle specific tasks such as flight booking or bus Keywords route enquiry [23, 46]. One of the most obvious drawbacks for the Human-computer conversation; big data; rank optimization domain-specific service is that the conversation cannot go beyond the domain of the system, and that the way to function is nearly impossible to be generalized to open domain. It is only recently Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed that non-task-oriented dialogue, a.k.a. open-domain conversation, for profit or commercial advantage and that copies bear this notice and the full cita- has been attracting the attention for its functional, social, and enter- tion on the first page. Copyrights for components of this work owned by others than tainment roles [3, 2, 26, 14, 39, 20, 32, 9, 19, 48]. In this paper, we ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or re- publish, to post on servers or to redistribute to lists, requires prior specific permission set our target at creating a human-computer chat companion system and/or a fee. Request permissions from [email protected]. (i.e., a ChatBot) in the open domain. CIKM’16 , October 24-28, 2016, Indianapolis, IN, USA Building an open-domain ChatBot system to interact with hu- ⃝c 2016 ACM. ISBN 978-1-4503-4073-1/16/10. $15.00 mans is interesting but extremely challenging. Firstly, since people DOI: http://dx.doi.org/10.1145/2983323.2983360 649 • The 2nd contribution is that we examine the efficiency issue for the proposed system to be practical. We examine the difference of shallow learning and deep learning based candidate rankings for both base ranking and context-aware ranking. We investigate the trade-off between effectiveness and efficiency. We also run experiments to examine the effectiveness and effi- ciency of our proposed framework in comparison with several al- ternative methods. We are able to achieve the first-tier level for ef- fectiveness using the deep learning based ranking, compared with state-of-the-art approaches. Using the shallow learning based rank- ing, we can choose to outperform almost all rivals in terms of running time, while remain a relatively high effectiveness perfor- mance. We provide a useful perspective of view to launch a practi- (a). Query-reply w/o contexts. (b). Query-reply w/ contexts. cal system for online chat service. In Section 2 we start by reviewing previous work. Then we intro- Figure 1: Take a short multi-turn (2 turns) conversation for duce the conversation system framework, including pipeline com- example. It would be quite confusing and less meaningful to ponents, ranking paradigm with optimized combination designs. select a reply to the query alone without using the conversation We describe experiments and evaluations in Section 4, including context. It is natural to select a reply about travel to the query experimental setups, performance comparisons and result discus- when considering the context about travels in National Parks. sions. Finally we draw conclusions in Section 5. 2. RELATED WORK are free to say anything to the system, it is infeasible to prepare Early work on conversation systems is in general based on rules the knowledge for interaction before hand. Secondly, the possi- or templates and is designed for specific domains [34, 40]. These ble combinations of conversation status is virtually infinity so that rule-based approaches require no data or little data for training, conventional hand-crafted rules would fail to work for unexpected while instead require much manual effort to build the model, or to human utterances [35]. As mentioned, the emerging increase of big handcraft the rules, which is usually very costly. The conversation web data can greatly advance the development of conversation sys- structure and status tracking in vertical domains are more feasible tems in open-domain conversations. Owing to the diversity of the to learn and infer [46]. However, the coverage of the systems is al- web, a retrieval-based system can retrieve at least some replies for so far from satisfaction. Later, people begin to pay more attention almost any of the user input, and then returns a reply, which is a to automatic conversation systems in open domains [32, 9]. great advantage. To this end, we establish the conversation system From specific domains to open domains, the need for a big amoun- based on retrieval-and-ranking based method. t of data is increasing substantially to build a conversation sys- In general, there are two typical scenarios for computers to un- tem. As information retrieval techniques are developing fast, re- derstand conversations: 1) single-turn and 2) multi-turn conversa- searchers obtain promising achievements in (deep) question and tion. A single-turn conversation may refer to the beginning of a answering system. In this way, an alternative approach is to build (multi-turn) conversation, or a dialogue shift from the current con- a conversation system with a knowledge base consisting of a num- versation. A multi-turn conversation usually denote an on-going ber of question-answer pairs. Leuski et al. build systems to s- conversation which lasts for several turns. For multi-turn conversa- elect the most suitable response to the current message from the tion, the series of conversation history can be utilized as additional question-answer pairs using a statistical language model in cross- information, namely “contexts”. Single-turn conversations are easy lingual information retrieval [16], but have a major bottleneck of to understand. No context information is available, nor necessary. the creation of the knowledge base (i.e., question-answer pairs) We illustrate a simple multi-turn scenario in Figure 1, where con- [17].

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