The document discusses the implementation of chatbots as an alternative to FAQ pages on websites, highlighting the limitations of traditional customer support and past chatbot technologies. It proposes a model utilizing deep learning for intent and entity recognition to enhance user interaction and response accuracy, while incorporating both text and voice capabilities. The study emphasizes that modern chatbots can significantly improve user experience by providing natural conversational interactions, thereby replacing outdated FAQ systems.

1. Implementation of FAQ Pages using Chatbot
Sarath Nair ,Dept. of CSE,BMSIT&M,Bangalore,Karnataka
Sriram AD ,Dept. of CSE,BMSIT&M,Bangalore,Karnataka
A Mari Kirthima,Dept. of CSE,BMSIT&M,Bangalore
Tushar Sinha,Dept. of CSE,BMSIT&M,Bangalore,Karnataka
ABSTRACT
A website or an app is a customary way that a business adopts to provide
their services to their customer base. However, given the limited storage in the
phones, not many users will be willing to download an app to get their queries
addressed. Going to company website is also time consuming. In general, when
the consumers face issues, they reach out to the customer support. More often
than not, it takes a long waiting time to reach the customer service
representative. Not to mention, these calls are not always satisfactory.
Interacting with customers and retaining those customers becomes difficult for
the businesses with a wide audience to cater. Chatbots provide an option that
can be used by businesses to address the general queries of the user. These are
chat-based software that understand anything user types or says and accordingly
replies and takes actions. The recent developments in the field of artificial
intelligence have made chatbots more intelligent and adaptable for being a
substitute to FAQ pages.
Keywords—Chatbots, Artificial Intelligence, FAQ pages
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 16, No. 6, June 2018
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2. INTRODUCTION
FAQ page is section of a website that becomes a go-to destination for the customers. This
page is one of the most important pages on the website and helps the business increase its
online presence and drive qualified traffic to its site. Many companies have realized this and
have spent a lot of time and effort in improving the content of their FAQ pages and also the
way this content is being served to the user base. Chatbots offer a simple solution to this issue.
Chatbots have been there for some time now. They have been gaining a lot of popularity
ever since they hit the market. Despite this not a lot of companies have used them as a
replacement for their FAQ pages. The reason has something to do with the way chatbots have
been implemented in the past. The chatbots of the past have not been intelligent. These
chatbots have not showcased an ability to have human like conversation with the users. The
traditional chatbots have used buttons and decision tree models, a technique that has been
employed by call centers in past with limited success. In technical terms, these chatbots were
not conversational. There are lots of problems involved when users are forced to choose from
a set of options. It is likely that at some point of time during the conversation, the option they
need is not part of that set. Moreover, there is a chance that they try to ask question to a user in
a form that is not understood by the chatbot. Another issue worth considering is that if the user
changes his mind during a conversation, he won’t be able to go back on that decision and will
be left with no options but to start over. The industry needs a conversational chatbot now more
than ever.
In this paper, we aim to propose a model of a conversational chatbot that can be used by the
industry as a substitution for their dull FAQ pages. Deep learning concepts can be used to
create an intent and entity recognition model. Intents are classes that highlight the main
communicative essence in the user input. Entities are words of value in a user input. Each
user input may contain zero or more entities contain key information important to the ongoing
conversation. Common examples of entities include names of organizations, locations
and prices. Once the intent and the entities are recognized, the remaining part of the job is to
generate a suitable response from the knowledge base. Just replying in text format won’t be
enough, to reply more human like experience, it must provide voice interaction as well.
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 16, No. 6, June 2018
188 https://sites.google.com/site/ijcsis/
ISSN 1947-5500

3. RELATED WORKS
Most of the chatbots in the industry are rule based. The knowledge of these chatbots are
stored in the form of patterns and templates. When the user query matches one of the
patterns, the response stored in the <template> is sent to the user. The <pattern> could be a
simple sentence like “what is income tax?” or a string with regular expression like “what is *
?” .The <pattern> and <template> are handwritten.[1]
An inherent problem with this approach is finding an appropriate algorithm to match user
queries to a particular <pattern>. Eliza, one of the earliest chatbots created in 1964 at at the
MIT Artificial Intelligence Laboratory by Joseph Weizenbaum used an interesting approach.
Eliza used incremental parsing for pattern matching. All the words in the user input were
parsed and looked for in the dictionary. Each word was given a priority based on importance
and stored on a keyword stack. The word on the top of the keyword stack was tried to be
associated with one of the patterns. Based on the pattern match, a suitable reply stored in
template was generated. In case an association with a pattern could not be made, a default
reply like “I see”, “Please go on” was displayed. [2]
ALICE, a later implementation of chatbot, inspired by ELIZA used a different technique for
pattern matching. The knowledge of ALICE was stored in Graphmaster, a graph with nodes
and edges. The path to every leaf node is a sentence or a user query, the reply for which is
stored in that particular leaf node.
The problem with all the rule based approaches is that the rules must be provided by the
programmer .A lot of time and effort is required in writing such rules. [3]
PROPOSED WORK
A lot of developer’s time will be saved if they can utilize the large sets of chat logs on
various chat platforms like Twitter and Facebook. Instead of classifying the sets into questions
and responses, deep learning techniques can be used to recognize intents and entities in the
user input and map these intents and entities to a suitable response stored in a database.
As shown in Figure 1, a user starts the conversation by asking a question which is sent to
the ChatBot. The ChatBot then processes the input query and generates the response for the
user which is sent as a reply to the user.
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 16, No. 6, June 2018
189 https://sites.google.com/site/ijcsis/
ISSN 1947-5500

4. Fig 1: Block diagram of intent and entity recognition process
If the ChatBot is unable to find suitable answer or response for the input query it sends the
query to the admin. The admin can check the log file to find the unanswered queries and adds
the suitable response in the form templates and updates it back to the ChatBot. This
continuous process helps in learning through user interaction and if the same question is asked
again the ChatBot is able to answer it.
ChatBot controls conversation flow based on the context of the user’s requests and
responds with natural language phrases to provide direct answers, requests additional
information or recommended actions that can be taken.
Fig 2: Basic Conversation Flow
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 16, No. 6, June 2018
190 https://sites.google.com/site/ijcsis/
ISSN 1947-5500

5. Figure 2 provides a high level description of how a chat client could be used to leverage
natural language processing to assist with access to content or perform data queries.
PROPOSED WORK
A lot of developer’s time will be saved if they can utilize the large sets of chat logs on
various chat platforms like Twitter and Facebook. Instead of classifying the sets into questions
and responses, deep learning techniques can be used to recognize intents and entities in the
user input and map these intents and entities to a suitable response stored in a database.
As shown in Figure 1, a user starts the conversation by asking a question which is sent to
the ChatBot. The ChatBot then processes the input query and generates the response for the
user which is sent as a reply to the user.
Fig 1: Block diagram of intent and entity recognition process
If the ChatBot is unable to find suitable answer or response for the input query it sends the
query to the admin. The admin can check the log file to find the unanswered queries and adds
the suitable response in the form templates and updates it back to the ChatBot. This
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 16, No. 6, June 2018
191 https://sites.google.com/site/ijcsis/
ISSN 1947-5500

6. continuous process helps in learning through user interaction and if the same question is asked
again the ChatBot is able to answer it.
ChatBot controls conversation flow based on the context of the user’s requests and
responds with natural language phrases to provide direct answers, requests additional
information or recommended actions that can be taken.
Fig 2: Basic Conversation Flow
Figure 2 provides a high level description of how a chat client could be used to leverage
natural language processing to assist with access to content or perform data queries.
RESULT
Figure 3a: RASA Server
International Journal of Computer Science and Information Security (IJCSIS),
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7. Figure 3b: Flask Server
Figure 3a and 3b show the RASA server and Flask server. The Flask server
takes user input from client interface and passes it onto the RASA server.
Figure 4: RASA server’s response
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 16, No. 6, June 2018
193 https://sites.google.com/site/ijcsis/
ISSN 1947-5500

8. Figure 4 shows the RASA server’s response to a query in json format. The
figure shows how intents are ordered in terms of the confidence order.
Figure 5a: Interactive chat client interface
Figure 5b: Interactive chat client interface
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 16, No. 6, June 2018
194 https://sites.google.com/site/ijcsis/
ISSN 1947-5500

9. Figure 5a and 5b show the interactive chat client interface. It shows the send
button, speak button and listen button. These buttons are used for textual and
voice interaction with the Chatbot.
CONCLUSION
By enabling textual and voice interaction between Chatbot and the user, the
overall user experience is enhanced. Chatbots have shown the potential to
replace the website based implementation of FAQ pages. It is a technology that
can allow users to have natural conversations to access content and services.
Chatbot typically take the form of a chat client, leveraging natural language
processing to conduct a conversation with the user.
References
[1] Wallace, R. S. The Anatomy of A.L.I.C.E. Retrieved from
http://www.alicebot.org/anatomy.html
[2] Lokman, A. (2010). One-Match and All-Match Categories for Keywords
Matching in Chatbot. American Journal of Applied Sciences, 7(10), 1406–
1411. http://doi.org/10.3844/ajassp.2010.1406.1411
[3] AbuShawar, B., & Atwell, E. ALICE : Trials and Outputs. Retrieved from
http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1405-
55462015000400625
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 16, No. 6, June 2018
195 https://sites.google.com/site/ijcsis/
ISSN 1947-5500