What are the skills and tools needed to design and develop question answering systems?

1 QAS Types and Challenges

The first step to design a QAS is to understand the different types and challenges of question answering. QAS can be classified into four main categories: factoid, non-factoid, conversational, and visual. Factoid QAS can answer questions that have a single or a list of factual answers, such as "Who is the president of France?". Non-factoid QAS can answer questions that require reasoning, inference, or opinion, such as "Why is the sky blue?". Conversational QAS can engage in a dialogue with the user and maintain context and coherence, such as "What are some good movies to watch?". Visual QAS can answer questions that involve images or videos, such as "What color is the car in this picture?". Each type of QAS poses different challenges, such as data availability, question understanding, answer extraction, and user interaction.

2 QAS Architecture and Components

The second step to design a QAS is to define the architecture and components of the system. A typical QAS consists of four main components: question analysis, document retrieval, answer extraction, and answer presentation. Question analysis is the process of parsing, interpreting, and reformulating the user's question into a query that can be matched with relevant documents. Document retrieval is the process of finding and ranking documents that contain potential answers from a large collection of sources. Answer extraction is the process of locating and extracting the exact answer from the retrieved documents. Answer presentation is the process of formatting and displaying the answer to the user in a clear and concise manner.

3 QAS Skills and Tools

The third step to design a QAS is to acquire the skills and tools needed to implement each component of the system. QAS requires a combination of skills from natural language processing (NLP), information retrieval (IR), machine learning (ML), and human-computer interaction (HCI). For NLP, this includes text processing, parsing, semantic analysis, question reformulation, and answer generation. IR requires indexing, ranking, query expansion, and document summarization. ML necessitates classification, clustering, regression, neural networks, and deep learning. HCI involves user interface design, usability testing, and feedback collection. Popular tools for QAS development include NLTK (a Python library for NLP tasks), Elasticsearch (a distributed search engine), BERT (a pre-trained language model that can be fine-tuned for various NLP tasks) and Dash (a Python framework for creating interactive web applications). These tools can provide valuable insights for displaying QAS results in graphs, tables, and charts.

4 QAS Evaluation and Improvement

The fourth step to design a QAS is to evaluate and improve the performance and quality of the system. Intrinsic evaluation measures how well the system performs on metrics like accuracy, precision, recall, and F1-score, while extrinsic evaluation assesses how well it meets user needs and expectations in terms of relevance, satisfaction, and usefulness. To improve the system, data augmentation can be used to add more data or modify existing data to increase diversity and coverage. Error analysis can be utilized to identify and correct sources of errors in the system components. Additionally, feedback learning can be implemented to incorporate user feedback into the QAS to adapt and refine its behavior and output.

5 QAS Examples and Resources

The final step to design a QAS is to explore some examples and resources that can inspire and guide you in your QAS project. For instance, Google is the most popular web search engine that can answer various types of questions using natural language understanding, knowledge graphs, and machine learning. Watson is another AI platform that can answer complex questions in various domains such as business, health, education, and entertainment. QuAC is a dataset and a challenge for conversational question answering that involves asking and answering questions about a passage of text. Additionally, SQuAD is a dataset and a challenge for extractive question answering that involves finding a span of text in a paragraph that answers a question.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?