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 1 month ago
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Context: The Search problem
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GO-FOOD uses the ElasticSearch stack with restaurant and dish indexes to search for what the user types. However, this results in only exact text matches and at most, fuzzy matches. We wanted to create a holistic search experience that not only personalised search results, but also retrieved restaurants and dishes that were more relevant to what the user was looking for. This is being done by not only taking advantage of ElasticSearch features, but also developing a Query semantics engine.
Query Understanding: What & Why
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In the duration of this talk, you will learn about how we are taking advantage of word embeddings to build a Query Understanding Engine that is holistically designed to make the customer’s experience as smooth as possible. I will go over the techniques we used to build each component of the engine, the data and algorithmic challenges we faced and how we solved each problem we came across.
Joy Mustafi - Human-Machine Interaction through Multi-Modal Interface with Combination of Speech, Text, Image and Sensor DataJoy MustafiDirector and Principal ResearcherSalesforce
schedule 4 months agoSold Out!
In the context of human–computer interaction, a modality is the classification of a single independent channel of sensory input / output between a computer and a human. A system is designated uni-modal if it has only one modality implemented, and multi-modal if it has more than one. When multiple modalities are available for some tasks or aspects of a task, the system is said to have overlapping modalities. If multiple modalities are available for a task, the system is said to have redundant modalities. Multiple modalities can be used in combination to provide complementary methods that may be redundant but convey information more effectively. Modalities can be generally defined in two forms: human-computer and computer-human modalities.
With the increasing popularity of smartphones, the general public are becoming more comfortable with the more complex modalities. Speech recognition was a major selling point of the iPhone and following Apple products, with the introduction of Siri. This technology gives users an alternative way to communicate with computers when typing is less desirable. However, in a loud environment, the audition modality is not quite effective. This exemplifies how certain modalities have varying strengths depending on the situation. Other complex modalities such as computer vision in the form of Microsoft's Kinect or other similar technologies can make sophisticated tasks easier to communicate to a computer especially in the form of three dimensional movement.
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Computers utilize a wide range of technologies to communicate and send information to humans:
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- Audition – various audio outputs
- Tactition – vibrations or other movement
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- Olfaction (smell)
- Thermoception (heat)
- Nociception (pain)
- Equilibrioception (balance)
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- Pointing device
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- Speech recognition
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).