Introduction
$\dagger $ Corresponding author. This paper was partially supported by National Key Research and Development Program of China (No. 2017YFB0304100), National Natural Science Foundation of China (No. 61672343 and No. 61733011), Key Project of National Society Science Foundation of China (No. 15-ZDA041), The Art and Science Interdisciplinary Funds of Shanghai Jiao Tong University (No. 14JCRZ04). This work is licensed under a Creative Commons Attribution 4.0 International Licence. Licence details: http://creativecommons.org/licenses/by/4.0/.
Recently, dialogue and interactive systems have been emerging with huge commercial values BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , especially in the e-commerce field BIBREF6 , BIBREF7 . Building a chatbot mainly faces two challenges, the lack of dialogue data and poor performance for multi-turn conversations. This paper describes a fine-grained information retrieval (IR) augmented multi-turn chatbot - Lingke. It can learn knowledge without human supervision from conversation records or given product introduction documents and generate proper response, which alleviates the problem of lacking dialogue corpus to train a chatbot. First, by using Apache Lucene to select top 2 sentences most relevant to the question and extracting subject-verb-object (SVO) triples from them, a set of candidate responses is generated. With regard to multi-turn conversations, we adopt a dialogue manager, including self-attention strategy to distill significant signal of utterances, and sequential utterance-response matching to connect responses with conversation utterances, which outperforms all other models in multi-turn response selection. An online demo is available via accessing http://47.96.2.5:8080/ServiceBot/demo/.
Architecture
This section presents the architecture of Lingke, which is overall shown in Figure 1 .
The technical components include 1) coreference resolution and document separation, 2) target sentences retrieval, 3) candidate responses generation, followed by a dialouge manager including 4) self-matching attention, 5) response selection and 6) chit-chat response generation.
The first three steps aim at selecting candidate responses, and in the remaining steps, we utilize sentences from previous conversations to select the most proper response. For multi-turn conversation modeling, we develop a dialogue manager which employs self-matching attention strategy and sequential utterance-response matching to distill pivotal information from the redundant context and determine the most proper response from the candidates.
Usability and Analysis
In this section, we will discuss the usability of Lingke. In situation of lacking enough dialogue data such as when a new product is put on an online shop, Lingke only needs an introduction document to respond to customers. Because of the chit-chat response generation engine, Lingke can easily deal with any commodity-independent conversations. Thanks to our multi-turn model, Lingke will not get confused when customer gives incomplete questions which need to be understood based on context.
Figure UID17 - UID17 show two typical application scenarios of Lingke, namely, conversation record based and document-based ones, which vary based on the training corpus. Figure UID17 shows Linke can effectively respond to the customer shopping consultations. The customer sends a product link and then Lingke recognizes it, and when the customer asks production specifications Lingke will give responses based on information from the context and the conversation record. Figure UID17 shows a typical scenario when a customer consults Lingke about a new product. The customer starts with a greeting, which is answered by chit-chat engine. Then the customer asks certain features of a product. Note that the second response comes from a sentence which has a redundant clause, and main information the customer cares about has been extracted. In the third user utterance, words like “What" and “ZenBook Pro" are omitted, which can be deducted from the prior question. Such pivotal information from the context is distilled and utilized to determine proper response with the merit of self-matching attention and multi-turn modeling.
The user utterances of examples in this paper and our online demo are relatively simple and short, which usually aim at only one feature of the product. In some cases, when the customer utterance becomes more complex, for example, focusing on more than one feature of the product, Lingke may fail to give complete response. A possible solution is to concatenate two relevant candidate responses, but the key to the problem is to determine the intents of the customer.
Conclusion
We have presented a fine-grained information retrieval augmented chatbot for multi-turn conversations. In this paper, we took e-commerce product introduction as example, but our solution will not be limited to this domain. In our future work, we will add the mechanism of intent detection, and try to find solutions of how to deal with introduction document that contains more than one object.

Question: What datasets are used to evaluate the introduced method?
Answer: They used a dataset from Taobao which contained a collection of conversation records between customers and customer service staffs. It contains over five kinds of conversations,
including chit-chat, product and discount consultation, querying delivery progress and after-sales feedback.

Question: What are the results achieved from the introduced method?
Answer: Their model resulted in values of 0.476, 0.672 and 0.893 for recall at position 1,2 and 5 respectively in 10 candidates.