For robust ML and NLP model, training the chatbot dataset with correct big data leads to desirable results.
Chatbots are artificial intelligence software that simulates conversations with the user in natural language across various social interaction channels such as messaging applications, websites, and mobile applications or through the telephone. The global chatbot market size is forecasted to grow from US$2.6 billion in 2019 to US$ 9.4 billion by 2024 at a CAGR of 29.7% during the forecast period. The chatbot datasets are trained for machine learning and natural language processing models.
In retrospect, NLP helps chatbots training. The chatbots datasets require an exorbitant amount of big data, trained using several examples to solve the user query. However, training the chatbots using incorrect or insufficient data leads to undesirable results. As the chatbots not only answer the questions, but also converse with the customers, it becomes imperative that correct data is used for training the datasets.
Henceforth, here are the major 10 chatbot datasets that aids in ML and NLP models.
Yahoo Language Data
Yahoo Language Data is a form of question and answer dataset curated from the answers received from Yahoo. This dataset contains a sample of the “membership graph” of Yahoo! Groups, where both users and groups are represented as meaningless anonymous numbers so that no identifying information is revealed. Users and groups are nodes in the membership graph, with edges indicating that a user is a member of a group. The dataset consists only of the anonymous bipartite membership graph and does not contain any information about users, groups, or discussions.
Question-Answer dataset contains three question files, and 690,000 words worth of cleaned text from Wikipedia that is used to generate the questions, specifically for academic research.
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.
The ClariQ challenge is organized as part of the Search-oriented Conversational AI (SCAI) EMNLP workshop in 2020. This is a form of Conversational AI systems and series, with the main aim of to return an appropriate answer in response to the user requests.
NPS Chat Corpus
The NPS Chat Corpus is part of the Natural Language Toolkit (NLTK) distribution. It builds Python programs to work with human language data. It includes both the whole NPS Chat Corpus as well as several modules for working with the data.
The Multi-Domain Wizard-of-Oz dataset (MultiWOZ) is a fully-labeled collection of human-human written conversations spanning over multiple domains and topics.
Excitement Open Platform
The EXCITEMENT Open Platform (EOP) is a generic multi-lingual platform for textual inference made available to the scientific and technological communities.
HotpotQA is a question answering dataset featuring natural, multi-hop questions, with strong supervision to support facts to enable more explainable question answering systems.
Shaping Answers with Rules through Conversation (ShARC) is a form of question and answers dataset that answers questions through logical reasoning and by evaluating the performance of rule-based and machine learning baselines.
NQ is the dataset that uses naturally occurring queries and focuses on finding answers by reading an entire page, instead of relying on extracting answers from short paragraphs.