The Natural Language Decathlon: A Multitask Challenge for NLP
Deep learning has significantly improved state-of-the-art performance for natural language processing (NLP) tasks, but each one is typically studied in isolation. The Natural Language Decathlon (decaNLP) is a new benchmark for studying general NLP models that can perform a variety of complex, natural language tasks. By requiring a single system to perform ten disparate natural language tasks, decaNLP offers a unique setting for multitask, transfer, and continual learning.and is publicly available on github in order to use for tasks like Question Answering, Machine Translation, Summarization, Sentiment Analysis etc.
Outline/Structure of the Talk
- Introduction to DecaNLP
- Targeted NLP Tasks
- Open Source Collaboration on github
- Patents / Publications in NLP, Computer Vision, AI.
People will be able to understand different problems of NLP like:
1. Question Answering
2. Machine Translation
4. Natural Language Inference
5. Sentiment Analysis
6. Semantic Role Labeling
7. Relation Extraction
8. Goal-Oriented Dialogue
9. Semantic Parsing
10. Commonsense Reasoning
People will know about a unified Framework provided by decaNLP to solve different NLP tasks mentioned above.
People having basic knowledge of NLP, Machine Learning and Deep Learning.
Prerequisites for Attendees
Read basic stuff about NLP, Machine Learning, Deep Learning.
schedule Submitted 4 months ago
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Having multiple modalities in a system gives more affordance to users and can contribute to a more robust system. Having more also allows for greater accessibility for users who work more effectively with certain modalities. Multiple modalities can be used as backup when certain forms of communication are not possible. This is especially true in the case of redundant modalities in which two or more modalities are used to communicate the same information. Certain combinations of modalities can add to the expression of a computer-human or human-computer interaction because the modalities each may be more effective at expressing one form or aspect of information than others. For example, MUST researchers are working on a personalized humanoid built and equipped with various types of input devices and sensors to allow them to receive information from humans, which are interchangeable and a standardized method of communication with the computer, affording practical adjustments to the user, providing a richer interaction depending on the context, and implementing robust system with features like; keyboard; pointing device; touchscreen; computer vision; speech recognition; motion, orientation etc.
There are six types of cooperation between modalities, and they help define how a combination or fusion of modalities work together to convey information more effectively.
- Equivalence: information is presented in multiple ways and can be interpreted as the same information
- Specialization: when a specific kind of information is always processed through the same modality
- Redundancy: multiple modalities process the same information
- Complimentarity: multiple modalities take separate information and merge it
- Transfer: a modality produces information that another modality consumes
- Concurrency: multiple modalities take in separate information that is not merged
Computer - Human Modalities
Computers utilize a wide range of technologies to communicate and send information to humans:
- Vision - computer graphics typically through a screen
- Audition - various audio outputs
Adaptive: They MUST learn as information changes, and as goals and requirements evolve. They MUST resolve ambiguity and tolerate unpredictability. They MUST be engineered to feed on dynamic data in real time.
Interactive: They MUST interact easily with users so that those users can define their needs comfortably. They MUST interact with other processors, devices, services, as well as with people.
Iterative and Stateful: They MUST aid in defining a problem by asking questions or finding additional source input if a problem statement is ambiguous or incomplete. They MUST remember previous interactions in a process and return information that is suitable for the specific application at that point in time.
Contextual: They MUST understand, identify, and extract contextual elements such as meaning, syntax, time, location, appropriate domain, regulation, user profile, process, task and goal. They may draw on multiple sources of information, including both structured and unstructured digital information, as well as sensory inputs (visual, gestural, auditory, or sensor-provided).
Multi-Modal Interaction: https://www.youtube.com/watch?v=jQ8Gq2HWxiA
Gesture Detection: https://www.youtube.com/watch?v=rDSuCnC8Ei0
Speech Recognition: https://www.youtube.com/watch?v=AewM3TsjoBk
Assignment (Hands-on Challenge for Attendees)
Real-time multi-modal access control system for authorized access to work environment - All the key concepts and individual steps will be demonstrated and explained in this workshop, and the attendees need to customize the generic code or approach for this assignment or hands-on challenge.