Types of questions Factoid questions How many calories are there in two slices of apple pie? Most question answering systems focus on factoid questions, that can be answered with simple facts expressed in short texts. The answers to the questions usually can be expressed by a personal name, temporal expression, or location. Complex (narrative) questions: In children with an acute febrile illness, what is the efficacy of acetaminophen in reducing fever? »
What is the robustness of NLU? One thing we have to admit, deep learning models are easy to be fooled. In software development, robust programming is a style of programming that focuses on handling unexpected termination and unexpected actions. Similar in natural language understanding, the NLU system needs to be prepared for cases where the input data does not correspond to the expectations. The expectations are vary by the systems, in chatting bot, that usually means the system can handle offensive language, and in text classification, the system should ignore irrelevant information. »
Information extraction turns the unstructured information embedded in texts into structured data. There are many related applications around us now, say, when you apply for a job position, instead of filling out all the slots manually, just upload your resume and the system will automatically fill the information for you, though it’s not accurate sometimes(this happens a lot). Maybe that’s why the world still needs us : ) The goals of information extraction including: »
Generally, two kinds of dialog systems: task-oriented dialogue agents (help complete tasks) and chatbot (for conversation). Chatbot Some basic Concepts A dialogue is a sequence of turns, each a single contribution to the dialogue. Endpointing or endpoint detection: Spoken dialogue systems must detect whether a user is done speaking, so they can process the utterance and respond. Grounding means acknowledging that the hearer has understood the speaker. initiative: sometimes a conversation is completely controlled by one participant. »
Overview Natural Language Generation (NLG) is all about generating text, it’s part of Natural Language Processing (NLP), but in a difference branch to Natural Language Understanding (NLU). To be more specific, here are some tasks related to NLG: Machine Translation (Abstractive) Summarization Dialogue (chit-chat and task-based) Creative writing: storytelling, poetry-generation Freeform Question Answering (i.e. answer is generated, not extracted from text or knowledge base) Image captioning Key Component: Language Model To generate text, we rely on a language model, which predicts the next word given the words so far. »