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Gradient
 
Gradient
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Robot
Gradient

NLU datasets accelerating Conversational AI progress

May 2020

Lack of training data for various tasks related to conversational AI , has been a bottleneck in its progress & adoption. Slot-filling bots are too fragile to stand the test of the time, have shown glaring deficiencies which are tough to plug. Natural conversation requires more than just intent detection and entity extraction which most of the chatbots rely on; lacking the key elements of NLU(syntactic, semantic, pragmatic) capabilities because of lack of good quality training data. Creating, annotating, synthesising large datasets with quality & quantity good enough to build such capabilities, are expensive ,time consuming and requires skilled data annotators.

Finding the Elusive ‘U’ in NLU

Nov, 2019

Lack of training data for various tasks related to conversational AI , has been a bottleneck in its progress & adoption. Slot-filling bots are too fragile to stand the test of the time, have shown glaring deficiencies which are tough to plug. Natural conversation requires more than just intent detection and entity extraction which most of the chatbots rely on; lacking the key elements of NLU(syntactic, semantic, pragmatic) capabilities because of lack of good quality training data. Creating, annotating, synthesising large datasets with quality & quantity good enough to build such capabilities, are expensive ,time consuming and requires skilled data annotators.

Does your AI system ‘know’ what it does not know !

Nov, 2019

Question answering on unstructured data is considered a task, worthy of evaluating even a human learning a new language, and expected to be tough for AI systems to do so, with high precision. There has been a lot of progress in the field lately, the recent state of art algorithms have shown to be more accurate than human performance on some specific datasets like Squad.While these algorithms are effective, it has been observed that most of these rely on superficial information like local context similarity, global term frequency etc to extract answer from the documents.

Business Meeting
Library Book Shelves
Robot
Gradient

NLU datasets accelerating Conversational AI progress

May 2020

Lack of training data for various tasks related to conversational AI , has been a bottleneck in its progress & adoption. Slot-filling bots are too fragile to stand the test of the time, have shown glaring deficiencies which are tough to plug. Natural conversation requires more than just intent detection and entity extraction which most of the chatbots rely on; lacking the key elements of NLU(syntactic, semantic, pragmatic) capabilities because of lack of good quality training data. Creating, annotating, synthesising large datasets with quality & quantity good enough to build such capabilities, are expensive ,time consuming and requires skilled data annotators.

Finding the Elusive ‘U’ in NLU

Nov, 2019

Lack of training data for various tasks related to conversational AI , has been a bottleneck in its progress & adoption. Slot-filling bots are too fragile to stand the test of the time, have shown glaring deficiencies which are tough to plug. Natural conversation requires more than just intent detection and entity extraction which most of the chatbots rely on; lacking the key elements of NLU(syntactic, semantic, pragmatic) capabilities because of lack of good quality training data. Creating, annotating, synthesising large datasets with quality & quantity good enough to build such capabilities, are expensive ,time consuming and requires skilled data annotators.

Does your AI system ‘know’ what it does not know !

Nov, 2019

Question answering on unstructured data is considered a task, worthy of evaluating even a human learning a new language, and expected to be tough for AI systems to do so, with high precision. There has been a lot of progress in the field lately, the recent state of art algorithms have shown to be more accurate than human performance on some specific datasets like Squad.While these algorithms are effective, it has been observed that most of these rely on superficial information like local context similarity, global term frequency etc to extract answer from the documents.

Business Meeting
Library Book Shelves
Robot
Gradient

NLU datasets accelerating Conversational AI progress

May 2020

Lack of training data for various tasks related to conversational AI , has been a bottleneck in its progress & adoption. Slot-filling bots are too fragile to stand the test of the time, have shown glaring deficiencies which are tough to plug. Natural conversation requires more than just intent detection and entity extraction which most of the chatbots rely on; lacking the key elements of NLU(syntactic, semantic, pragmatic) capabilities because of lack of good quality training data. Creating, annotating, synthesising large datasets with quality & quantity good enough to build such capabilities, are expensive ,time consuming and requires skilled data annotators.

Finding the Elusive ‘U’ in NLU

Nov, 2019

Lack of training data for various tasks related to conversational AI , has been a bottleneck in its progress & adoption. Slot-filling bots are too fragile to stand the test of the time, have shown glaring deficiencies which are tough to plug. Natural conversation requires more than just intent detection and entity extraction which most of the chatbots rely on; lacking the key elements of NLU(syntactic, semantic, pragmatic) capabilities because of lack of good quality training data. Creating, annotating, synthesising large datasets with quality & quantity good enough to build such capabilities, are expensive ,time consuming and requires skilled data annotators.

Does your AI system ‘know’ what it does not know !

Nov, 2019

Question answering on unstructured data is considered a task, worthy of evaluating even a human learning a new language, and expected to be tough for AI systems to do so, with high precision. There has been a lot of progress in the field lately, the recent state of art algorithms have shown to be more accurate than human performance on some specific datasets like Squad.While these algorithms are effective, it has been observed that most of these rely on superficial information like local context similarity, global term frequency etc to extract answer from the documents.