An effective customer support chatbot depends on a number of factors. Like the way it is designed to convert the leads and how the bot responds. Amongst all the things, the most important thing that shows the competency of your chatbot is how it comprehends the questions of the customers.
An effective chatbot should know what the customer is saying to it and how to respond back adequately. In order to insert data in your chatbot, you need to give proper training to your customer support chatbot. This helps to sort out the customer requests without any human interference. Incorporating realistic and task-oriented dialog information is one of the most essential elements for the development of your chatbot.
If you train your chatbot in a good way, it will definitely perform well. In order to make your customer support chatbot constructive, you need to incorporate a huge amount of data so that your bot can independently resolve all the queries without any human intervention.
In order to design a well-structured customer support chatbot, you need to incorporate the following datasets to make it working productive.
Customer Support Chatbot Datasets for effective training
In order to boost the services of your chatbot, we suggest you some of the best techniques that have been tested by our experts.
- The Ubuntu Dialogue Corpus is considered to be one of the finest quality datasets. It provides one million two-person conversations that have been taken out from several issues related to Ubuntu. So the full dataset consists of almost 9 lac dialogues and 100,000,000 word count. This shows the diversity of the dialogue range that Ubuntu Dialogue Corpus offers to you.
- Another well-known dataset for your customer support chatbot is the Customer Support on Twitter. We know that twitter plays a vital role for your business expenditions. In order to deal with different brands on Twitter, we recommend you the dataset on Kaggle. This software includes almost 3 million tweets. Kaggle helps to answer from a considerable number of brands on Twitter.
- Declarative techniques in Customer Support chatbot dataset is an essential element for questions related to travelling. Different customers ask questions related to different genres. You have to provide a diverse range of dataset to your customer support chatbot so that the users do not have to go to some other site for getting answers to their queries.
- Break is another set of data that will help to the comprehending of difficult tasks. It is comprised of about 83,900 natural language questions. Break contains an updated version of Question Decomposition Meaning Representation (QDMR). So it contains the natural question along with the QDMR representation.
- Another dataset for your customer support chatbot is the CommonsenseQA. It is comprised of almost 12,102 questions and in each question there is one correct answer and the rest of four are the distracting answers. This dataset consists of two training sets. A random assignment and a question token assignment.
- The dataset, CoQA, also known as Construction of Conversational Question Answering. It contains around 127,000 questions along with the answers. This data has been acquired from the eight thousand conversions which include the text paragraphs from several domains.
- HotpotQA consists of natural multi-skip questions. The significance of fixing such data in your customer support chatbot is that it gives heed to the supporting facts in order to permit for more direct question answering systems. So the HotpotQA consists of around 113,000 QA pairs acquired from Wikipedia.
- OpenBookQA is used to reach the human understanding of a particular subject. This dataset consists of 1329 scientific facts. Around 6,000 questions are being focused on the comprehension of the facts and can be used in some new situations.
- Natural Questions (NQ), a corpus that is quite helpful for the training and evaluation of open-ended questions. NQ consists of around 300,000 questions with the human-annotated answers that have been extracted from the Wikipedia pages. Moreover, it consists of 16,000 instances the answers of which have been provided by five different annotators. Thus, NA is quite helpful for checking the performance of the QA systems that have been learned.
- The next dataset for our customer support chatbot is the NewsQA. This dataset is useful to help the researchers in order to create algorithms that can solve several questions that need the reasoning skills. If calculated, NewsQA consists of 120,000 questions with answers too.
- QASC is a dataset that contains around 10,000 8-channel multiple choice questions. The focus of this dataset is on the sentence composition.
- Quora questions is another astounding dataset that contains several pairs of question texts. It consists of almost 400,000 lines of questions.
- RecipeQA, a dataset essential for learning a number of recipes. RecipeQA contains a wide range of questions with answers as well. There are around 36,000 pairs of questions related to 20,000 unique recipes. In addition, this dataset provides images of the recipes with instructions to thoroughly go through the process of some recipe that you want to learn.
- TREC QA Collection: A dataset that is necessary for your customer support chatbot is the TREC QA collection. This dataset has set a record for its track. It is helpful for getting answers for both the open-ended and close-ended questions.
- Yahoo Language Data, will present questions and answers that have been obtained from Yahoo.
- Maluuba goal-oriented dialogue contains information in which your customer support chatbot aims to complete some task. It is particularly concerned with finding a flight or booking some reservation in a hotel. According to the data saved in this dataset, around 250 hostels and flights are saved in it.
- OPUS: This dataset helps to translate different texts that have been taken from the internet. Moreover, OPUS assists in the conversion of online data in order to add the linguistic annotation. In this way, OPUS makes it easy to give people with an already available corpus. So your customer support chatbot finds a number of datasets through OPUS.
Publicly Available datasets can be the ideal way for getting training data for your customer support chatbot. As discussed, there are a number of highly significant publicly available datasets. Among these the most popular datasets are as following:
- Kaggle Linguistic Dataset
- Yahoo!Questions and Answers
- Amazon Reviews
In order to make your chatbot competent, past conversations with your customers play a vital role. We can say that your experience with the customers is like a treasure house for the betterment of your business and customer support chatbot. Reading the conversations of a chatbot with users will analyze the performance of your chatbot. If the bot needs any amendments, just go through the previous data and you will get some idea.
How to organize your training data?
Once you collect the data, then there is a need to properly arrange it. It is quite normal that a large number of customers ask similar questions to your chatbot. From the selected data, just notice the repeated sentences that have been used by the customers. This will help you to train your chatbot using some keywords so that the customer support chatbot replies back quickly.
Collecting the data and then organizing it can be a time-taking task. Moreover, you become exhausted. To cope up with such problems, just make use of annotation. For instance, if a customer asks the bot that he/she has forgotten their password, the chatbot will focus on the word ‘password’. By using the word, password, you can easily search out the conversations of customers with the chatbot that deals with problems related to the password setting. This will help you to search for any conversation by using some keywords.
Interpreting the above example, you need to use some keywords such as ‘pwd’, ‘access’, ‘username’, or simply ‘password’. By writing a keyword, you will be able to find examples related to the keyword being typed.
Training your Customer Support Chatbot
After structuring the data, the next task is to train your chatbot. This definitely depends on the platform that you are using for the development of the chatbot. Another factor is the intent for each use case plus adding examples of sentences.
Although this step is helpful it can be difficult to continue. This happens when you have multiple intents and the training examples are greater in number for each intent. Many platforms contain such files that may have sensitive data, so be careful while uploading such files.
The best way to make improvements in the performance of your chatbot is through checking the conversations being held between a chatbot and a customer. Leaving all the tasks on the chatbot is not a good way to lead your opponents. Instead frequently checking the performance of your customer support chatbot helps to upgrade your bot. Check and balance make things right. A chatbot is computer software but it can also make mistakes and you have to take care of your chatbot. Google, Microsoft, and many other development platforms have made it easier to manage your bot to go deep inside into the minds of customers, what they expect from your brand, what changes you should make? Just stay active to grow your business and keep striving to lead your competitors