1. Huawei CBG AI Challenges
Parkhomenko Denis, Bankevich Sergey,
Korikov Kirill, Bakarov Amir

2. Huawei CBG AI Challenges
Computer Vision Challenges
Speech & Language Challenges
NLU
ASR

3. How to make neural net lighter?
State-of-the-art neural nets are very complex in terms of
- calculation
- size
How to incorporate them
in so small chips?
ImageNet 1K validation set accuracy

4. How to make neural net lighter?
Tensor decomposition:
Filter quantization, dictionary based convolutions:
Target platform optimization:
- deep knowledge of CPU/TPU architecture
- vectorization, intrinsics, code optimization
R&D in new methods of matrix, tensors decomposition
Optimal parameter search
Consider functional spaces C1(X,Y), C2(X,Y).
For any given model params θ=(θ1,…, θN) and
model f1(x,θ)∈C1 find f2∈C2 such that:
𝑓 2 (x)= 𝑎𝑟𝑔𝑚𝑖𝑛 𝑓 2 ∈ 𝐶 2 ( 𝐸 𝑥~𝑋 || 𝑓 1 (𝑥,𝜽)− 𝑓 2 (𝑥)||)
 If you good at low-level programming

5. Optical character recognition
Task 1: Text detection in image
Task 2: Text recognition in cropped image
Task 3: End-to-end detection+recognition
Task 4: Inpaiting
Humans are still better there [

6. Huawei CBG AI Challenges
Computer Vision Challenges
Speech & Language Challenges
NLU
ASR

7. Dialogue System

8. Amazon Alexa Skill Builder Interface
Intent Detection
Amazon Alexa Skill Builder Interface

9. Whole-sentence features
Intent Detection
Corpus
Word vector
Neural network
Whole-sentence features
Rule-based heuristics
...
Word-level features:
Entities information
Syntax parsing features
Word-level vectors
softmax
More networks
External information:
Dictionary
Sentence-level vectors
Intent Classification
Network selection:
RNN
CNN
Attention / Transformer …
Sentence vector

10. NLU Challenges Deep learning model needs a lot of labeled data
For our skills we could use assessors to generate and classify corpus
But for third-party skills we could rely only on provided corpus (usually, tens of samples)
Is it possible to build a good classifier using such a small amount of data?

11. Challenge Deep learning model needs a lot of labeled data
For our skills we could use assessors to generate and classify corpus
But for third-party skills we could rely only on provided corpus (usually, tens of samples)
Is it possible to build a good classifier using such a small amount of data?
Example:
10 samples of labeled data
100 samples of unlabeled data
train the model on 100 samples and transfer labels

12. More challenges Word sense disambiguation Cross-lingual transfer
Integration of knowledge graphs to supervised models
Anaphora and coreference resolution
Chit-chatting support
Personalization of conversational agents …

13. Huawei CBG AI Challenges
Computer Vision Challenges
Speech & Language Challenges
NLU
ASR

14. ASR task Convert audio input to text output
Applications
Voice assistants (phone, home, car)
Recording/voice input transcription
Movie captions

15. ASR pipeline Feature extraction Acoustic model: morphemes/letters
Language model, decoder: text
Postprocessing

16. ASR components Support specific input conditions Language, accent
Close/far field
Deal with noise, multiple people speaking, low volume/quality
Different hardware

17. ASR components Support specific input conditions
Provide specific output properties
Normalization
Domains

18. ASR components Support specific input conditions
Provide specific output properties
Related and relative tasks
Voice activity detection
Trigger phrase
Direct classification

19. ASR challenges Speaker diarization, cocktail party, denoise
Flexible language model
Handling variety of accents
ASR on device
Text normalization
Optimization for production: C/C++, low-level