Let Android dream electric sheep: Making emotion model for chat-bot with Python3, NLTK and TensorFlow

Let Android dream electric sheep: Making emotion model for chat-bot with Python3, NLTK and TensorFlow
Let Android dream electric sheep: Making emotion model for chat-bot with Python3, NLTK and TensorFlow Jeongkyu Shin Lablup Inc.
Let Android dream electric sheep: Making emotion model for chat-bot with Python3, NLTK and TensorFlow Jeongkyu Shin Lablup Inc. Illustration * © Idol M@aster / Bandai Namco Games. All rights reserved.
–Bryce Courtenay's The Power of One “First with the head, then with the heart.”
Today’s focus ▪ NLP and Sentiment: Big problems when making chatbots ▪ Natural Language Understanding ▪ SyntaxNet and DRAGNN ▪ Emotion reading ▪ SentiWordNet and SentiSpace[1] ▪ Emotion simulation ▪ ML Sentiment engine [1] Our own definition for sentimental state space Illust: http://www.eetimes.com/document.asp?doc_id=1324302
I’m not sure but I’ll try to explain the whole process I did
And I assume that you already have experience / knowledge about machine learning Illustration *(c) marioandluigi97.deviantart.com
Things that will not be covered today ▪ Phase space / embedding dimension ▪ Recurrent Neural Network (RNN) ▪ GRU cell / LSTM cell ▪ Multi-layer stacking ▪ Batch process for training ▪ Vector representation of language sentence ▪ Sequence-to-sequence model ▪ Word2Vec
So we will go through ▪ Understanding Language ▪ Reading Emotion ▪ Mimicking Sentiment ▪ And make it more complex for fun © Sega. http://www.sega.com Image from Sonic Wikia : http://sonic.wikia.com/wiki/Sonic_the_Hedgehog
It’s even hard for humanbeings. Let’s start from Language Understanding:
Lexical Output Chat-bots with Machine Learning Context Analyzer Natural Language Processor Response Generator Decision maker Sentence To vector converter Deep-learning model (RNN / sentence-to-sentence) Knowledgebase (useful with TF/IDF ask bots) Per-user context memory Lexical Input Deep-learning model SyntaxNet / NLU (Natural Language Understanding) Today’s focus!
Understanding Languages ▪ The structure of language ▪ “Noun” and “Verb” ▪ “Context” ▪ POS (Part-of-speech) ▪ Roles for the words ▪ Added as tags ▪ Only one meaning in the current sentence context ▪ Generalized POS tags ▪ Some POS tags are very common (noun, verb, …) ▪ Others? Quite complicated!
SyntaxNet (2016) ▪ Transition-based framework for natural language processing ▪ Feature extraction ▪ Representing annotated data ▪ Evaluation ▪ End-to-end implementation using deep learning ▪ No language-awareness/dependencies: data-driven ▪ Interesting points ▪ Found general graph structure between different human languages (2016-7) ▪ http://universaldependencies.org *github.com/tensorflow/tensorflow
Generating NLP with SyntaxNet Obtaining Data POS Tagging Training SyntaxNet POS tagger Dependency parsing Transition-based Parsing Training Parser
SyntaxNet implementation ▪ Transition-based dependency parser ▪ SHIFT, LEFT ARC, RIGHT ARC ▪ “deviation” ▪ Configuration+Action ▪ Training ▪ Local pre-training / global training *Kong et al., (2017)
SyntaxNet Architecture *github.com/tensorflow/tensorflow
NMT / PBMT / GNMT ▪ Neural Machine Translation ▪ End-to-end learning for automated translation ▪ Requires extensive computation both training and inference ▪ GNMT ▪ LSTM Network w/ 8 encoder & 8 decoder ▪ Much better than PBMT (Phase-Based Machine Translation) *Wo et al., (2016)
DRAGNN (2017) ▪ Dynamic Recurrent Acyclic Graphical Neural Networks (Mar. 2017) ▪ Framework for building multi-task, fully dynamically constructed computation graphs ▪ Not GAN (Generative Adversarial Network)! ▪ Supports ▪ Training and evaluating models ▪ Pre-trained analyze models (McParsey) for 40 language ▪ Except Korean. (of course; ) ▪ Problem? ▪ Python 2 (like most NLP toolkits) *Kong et al., (2017)
*Android meets TensorFlow, Google I/O (2017)
Model differences ▪ DRAGNN[1]: End-to-end, deep recurrent models ▪ Use to extend SyntaxNet[2] to be end-to-end deep learning model ▪ TBRU: Transition-Based Recurrent Unit ▪ Uses both encoder and decoder ▪ TBRU-based multi-task learning : DRAGNN ▪ SyntaxNet: Transition-based NLP ▪ Can train SyntaxNet using DRAGNN framework [1] Kong et al., (2017) [2] Andor et al., (2016)
Encoder/Decoder (2 TBRU) Bi-LSTM Tagging (3 TBRU Y1 Y2 Y3 Y4 Y5 Y1 Y2 Y3 Y4 Y Transition Based Recurrent Unit (TBRU) Network Cell Discrete state Recurrence fcn Input embeddings network activations Figure 1: High level schematic of a Transition-Based Recurrent Unit ( TBRU ▪ Transition-based recurrent unit ▪ Discrete state dynamics: allow network connections to be built dynamically as a function of intermediate activations ▪ Potential of TBRU: extension and combination ▪ Sequence-to- sequence ▪ Attention mechanisms ▪ Recursive tree-structured models Encoder/Decoder (2 TBRU) Bi-LSTM Tagging (3 TBRU) Y1 Y2 Y3 Y4 Y5 Y1 Y2 Y3 Y4 Y5 Stack-LSTM (2 TBRU) Y1 Y2 Y3 Y4 Y5 Transition Based Recurrent Unit (TBRU) Network Cell Discrete state Recurrence fcn Input embeddings network activations Figure 1: High level schematic of a Transition-Based Recurrent Unit (TBRU), and common network Encoder/Decoder (2 TBRU) Bi-LSTM Tagging (3 TBRU) Y1 Y2 Y3 Y4 Y5 Y1 Y2 Y3 Y4 Y5 Transition Based Recurrent Unit (TBRU) Network Cell Discrete state Recurrence fcn Input embeddings network activations Figure 1: High level schematic of a Transition-Based Recurrent Unit (TBRU architectures that can be implemented with multiple TBRUs. The discrete recurrences and ﬁxed input embeddings, which are then fed through a network c an action which is used to update the discrete state (dashed output) and provid consumed through recurrences (solid output). Note that we present a slightly sim LSTM (Dyer et al., 2015) for clarity. NLP problems; Dyer et al. (2015); Lample et al. (2016); Kiperwasser and Goldberg (2016); Zhang et al. (2016); Andor et al. (2016), among others. tures by providing a ﬁn puts that ‘attend’ to relev space. Unlike recursive*Kong et al., (2017)
Parsey McParseface ▪ Parsey McParseface (2017) ▪ State-of-art deep learning-based text parser ▪ Performance comparison Model News Web Questions Martins et al. (2013) 93.10 88.23 94.21 Zhang and McDonald (2014) 93.32 88.65 93.37 Weiss et al. (2015) 93.91 89.29 94.17 Andor et al. (2016)* 94.44 90.17 95.40 Parsey McParseface 94.15 89.08 94.77 Model News Web Questions Ling et al. (2015) 97.44 94.03 96.18 Andor et al. (2016)* 97.77 94.80 96.86 Parsey McParseface 97.52 94.24 96.45 POS (part-of-speech) tagging For different language domains *github.com/tensorflow/tensorflow
McParseface model / DRAGNN framework *github.com/tensorflow/tensorflow
Where is Korean? ▪ Korean language-specific characteristics ▪ Order / sequence is not important / Distance is important ▪ However, SyntaxNet works even with raw Korean sequence! ▪ Even Japanese translation is supported ▪ Better solution? ▪ Now working on creating preprocessing NM for increase DRAGNN performance ▪ With `Josa` ML © Flappy Pepe by NADI games
Now, let’s move to the emotion reading part. Looks easier but hard, in fact.
Problems for next-gen chatbots ▪ Hooray! Deep-learning based chat bots works well with Q&A scenario! ▪ General problems ▪ Inhuman: restricted for model training sets ▪ Cannot "start" conversation ▪ Cannot handle continuous conversational context and its changes ▪ And, “Uncanny Valley” ▪ Inhuman speech / conversation. ▪ Why? How?
© http://www.fanuc.eu © http://muscleduck.deviantart.com
So, let’s just focus on the details of humanbeings : What makes us human? imdb.com ingorae.tistory.com/1249
I've seen things you people wouldn't believe. Attack ships on fire off the shoulder of Orion. I watched C-beams glitter in the dark near the Tannhäuser Gate. All those moments will be lost in time, like tears in rain. Time to die.
Lexical Output Sentence generatorDeep-learning model (sentence-to-sentence + context-aware word generator) Emotion engine Grammar generator Context memory Knowledge engine Emotion engine Context parser Tone generator Disintegrator Response generatorNLP + StV Context analyzer+Decision maker Lexical Input Today’s focus!
Conversational context locator ▪ Using Skip-gram and bidirectional 1-gram distribution in recent text ▪ I ate miso soup this morning. => Disintegrate first ▪ Bidirectional 1-gram set (reversible trigram): {(I,miso soup),Eat}, {(eat,today),miso soup}, {(miso soup,morning),today} ▪ Simplifying: {(,<FOOD>),<EAT>}, {(<EAT>,Today),<FOOD>}, {(<FOOD>,morning),Today} ▪ Distribution: more simplification is needed ▪ {(,<FOOD>), <EAT>}, {(<TIME:DATE>,<EAT>), <FOOD>}, {(<FOOD>,<TIME:DAY>),< TIME:DATE>} ▪ Now we can calculate multinomial distribution I Today MorningMiso soupEat Today Morning<FOOD><EAT> <TIME:DATE> <TIME:DAY><FOOD><EAT> *I’ll use trigram as abbreviation of reversible trigram
Conversational context locator ▪ Using Skip-gram and bidirectional 1-gram distribution in recent text ▪ 나는 오늘 아침에 된장국을 먹었습니다. => Disintegrate first ▪ Bidirectional 1-gram set: {(나,아침),오늘}, {(오늘,된장국),아침}, {(아침,먹다),된장국} ▪ Simplifying: {(,아침),오늘}, {(오늘,<FOOD>),아침}, {(아침,<EAT>),<FOOD>} ▪ Distribution: more simplification is needed ▪ {(,<TIME:DAY>), <TIME:DATE>}, {(<TIME:DATE>,<FOOD>), <TIME:DAY>}, {(<TIME:DAY> ,<EAT>),<FOOD>} ▪ Now we can calculate multinomial distribution 나 오늘 아침 된장국 먹다 오늘 아침 <FOOD> <EAT> <TIME:DATE> <TIME:DAY> <FOOD> <EAT> *I’ll use trigram as abbreviation of reversible trigram
Conversational context locator ▪ Training context space ▪ Context-marked sentences (>20000) ▪ Context: LIFE / CHITCHAT / SCIENCE / TASK ▪ Prepare Generated trigram sets with context bit ▪ Train RNN with 1-gram-2-vec ▪ Matching context space ▪ Input trigram sequence to context space ▪ Take the dominator axis ▪ Using Skip-gram and trigram distribution in recent text ▪ {(,<TIME:DAY>), <TIME:DATE>} ▪ {(<TIME:DATE>,<FOOD>), <TIME:DAY>} ▪ {(<TIME:DAY>,<EAT>),<FOOD>} ▪ With distribution ▪ Calculate maximum likelihood significance and get significant n-grams ▪ Uses last 5 sentences
For better performance ▪ Characteristics of Korean Language ▪ Distance between words: important ▪ Sequence between words: not important ▪ Different from English ▪ How to read more contextual information from longer text? (e.g. Documents) ▪ Change from trigram to in-range tri pairs ▪ I ate miso soup this morning: ▪ In range 1: {(,<FOOD>), <EAT>} ▪ In range 2: {(<TIME:DATE>), <EAT>} ▪ In range 3: {(<TIME:DAY>), <EAT>} ▪ Heavily depends on the length of original sentence ▪ Short? ▪ Long? <TIME:DATE> <TIME:DAY><FOOD><EAT>
Emotion engine ▪ Input: text sequence ▪ Output: Emotion flag (6-type / 3bit) ▪ Training set ▪ Sentences with 6-type categorized emotion ▪ Positivity (2), negativity (2), objectivity (2) ▪ Uses senti-word-net to extract emotion ▪ 6-axis emotion space by using Word2Vec model ▪ Current emotion indicator: the most weighted emotion axis using Word2Vec model Illustration *(c) http://ontotext.fbk.eu/ [0.95, 0.05, 0.11, 0.89, 0.92, 0.08] [1, 0, 0, 0, 0, 0] 0x01 index: 1 2 3 4 5 6 Position in senti-space:
Making emotional context locator ▪ Similar to conversational context locator ▪ Just use 1-gram from input ▪ Add the corresponding word vector on emotion space ▪ How to? ▪ Use NLTK python library ▪ NLTK has corpora / data for SentiWordNet ▪ Also gives download option! import nltk nltk.download() Downloading NLTK dataset
Making emotional context locator ▪ Get emotional flag from sentence Sample test routine for Sentimental state from nltk.corpus import sentiwordnet as swn def get_senti_vector(sentence, pos=None): result = dict() for s in sentence.split(' '): if s not in result.keys(): senti = list(swn.senti_synsets(s.lower(), pos)) if len(senti) > 0: mostS = senti[0] result[s] = [mostS.pos_score(), 1.0- mostS.pos_score(), mostS.neg_score(), 1.0- mostS.neg_score(), mostS.obj_score(), 1.0 - mostS.obj_score()] return result {'I': [0.0, 1.0, 0.25, 0.75, 0.75, 0.25], 'happy': [0.875, 0.125, 0.0, 1.0, 0.125, 0.875], 'super': [0.625, 0.375, 0.0, 1.0, 0.375, 0.625], 'surprised': [0.125, 0.875, 0.25, 0.75, 0.625, 0.375]} {'Hello': [0.0, 1.0, 0.0, 1.0, 1.0, 0.0], 'I': [0.0, 1.0, 0.0, 1.0, 1.0, 0.0], 'am': [0.0, 1.0, 0.0, 1.0, 1.0, 0.0], 'happy': [0.875, 0.125, 0.0, 1.0, 0.125, 0.875], 'was': [0.0, 1.0, 0.0, 1.0, 1.0, 0.0], 'super': [0.0, 1.0, 0.0, 1.0, 1.0, 0.0], 'surprised': [0.125, 0.875, 0.0, 1.0, 0.875, 0.125]} sentence = "Hello I am happy I was super surprised" result = get_senti_vector(sentence) Adj. only All morpheme
Creating Korean SentiWordNet ▪ Procedure to generate Korean SentiWordNet corpus for i in swn.all_senti_synsets(): data.append(i) <maimed.s.01: PosScore=0.0 NegScore=0.0> <fit.a.01: PosScore=0.5 NegScore=0.0> <acceptable.s.04: PosScore=0.25 NegScore=0.0> <suitable.s.01: PosScore=0.125 NegScore=0.0> <worthy.s.03: PosScore=0.875 NegScore=0.0> <unfit.a.01: PosScore=0.25 NegScore=0.0> <불구의.s.01: PosScore=0.0 NegScore=0.0> <알맞다.a.01: PosScore=0.5 NegScore=0.0> <만족스럽다.s.04: PosScore=0.25 NegScore=0.0> <적합하다.s.01: PosScore=0.125 NegScore=0.0> <훌륭하다.s.03: PosScore=0.875 NegScore=0.0> <부적합하다.a.01: PosScore=0.25 NegScore=0.0> 2. Translate words into Korean3. Treat synonym <불구의.s.01: PosScore=0.0 NegScore=0.0> <알맞다.a.01: PosScore=0.5 NegScore=0.0> <적합하다.a.01: PosScore=0.5 NegScore=0.0> <어울리다.a.01: PosScore=0.5 NegScore=0.0> <만족스럽다.s.04: PosScore=0.25 NegScore=0.0> <적합하다.s.01: PosScore=0.125 NegScore=0.0> <훌륭하다.s.03: PosScore=0.875 NegScore=0.0> <부적합하다.a.01: PosScore=0.25 NegScore=0.0> 4. Choose the score from ‘representative word’ 1. Get every synsets from sentiwordnet data
Tips to create Korean SentiWordNet ▪ Get open dictionary files (stardict(.ifo) / mdict (.mdx) format) ▪ You will find non-free dictionary data files on the web. Use them with your own risk. ▪ Convert them to .csv format using pyglossary (https://github.com/ilius/pyglossary) package ▪ https://github.com/ilius/pyglossary/blob/master/doc/Octopus%20MDi ct/README.rst will help understanding and manipulating .mdx file. ▪ Use the file for automatic translation of sentiwordnet data
Reading emotion with SentimentSpace ▪ Creating emotion space ▪ 1. Generate word space using word2vec model ▪ 2. Substitute word to SentiWordNet set ▪ 3. Now we get SentimentSpace! ▪ 4. Get the emotion state by giving disintegrated word set into SentimentSpace ▪ Focuses on reading emotion ▪ Final location on WordVec space = Average sentivector of nearest neighbors *SentimentSpace: our definition / approach to simulate emotion. SentimentSpace: WordVec Space with folded 2-sentiment dimension [.85, .15, .0] [.75, .05, .20] [.65, .15, .20] [.25, .10, .65]
Unfolding SentimentSpace ▪ Unfolded SentimentSpace ▪ Near nodes = Similar sentiments ▪ Great representation with serious problem ▪ Value resolution ▪ `Forgotten emotion` [.85, .15, .0] [.75, .05, .20] [.25, .15, .20] [.25, .10, .65] acceptable fit Unfavorable unsatisfactory objectivity
Reading emotion from sentence 1. Extract sentiwords from sentence 2. Locate the sentence on sentispace via Vector-sum of senti-words 3. Average the senti-state values on the point § Why using wordvec space instead of sentispace directly? § Resolution problem on sentiword dataset sentence = "Hello I am happy I was super surprised” fragments = (‘I’, ‘happy’, ‘super’, ‘surprised’)
Tips for SentimentSpace ▪ When picking the best match from candidates ▪ e.g. fit ➜ ▪ 1. Just pick the first candidate from senti sets ▪ 2. Calc the average Pos/Neg scores- [ 0.25, 0 ] ▪ When generating Korean SentiWordNet corpus ▪ 1. Do not believe the result. You will need tremendous amount of pre / postprocessing ▪ SentimentSpace is very rough. Keep in mind to model the emotion engine <fit.a.01: PosScore=0.5 NegScore=0.0> <acceptable.s.04: PosScore=0.25 NegScore=0.0> <suitable.s.01: PosScore=0.125 NegScore=0.0> <worthy.s.03: PosScore=0.875 NegScore=0.0>
Do we really know what emotion is? And, mimic the sentiment. © Sports chosun
–Ridley Scott’s blade runner "More human than human" is our motto. (c) http://www.westword.com/arts/the-top-five-best-futurama-episodes-ever-5800102
Modeling emotion model ▪ Goal: ▪ Simulating ‘emotion’ of bot ▪ What is emotion? ▪ How emotion changes? ▪ How? ▪ Define ‘emotion changes’ as changes of SentimentSpace paremeters ▪ Deep-learning model of parameter transitions by conversation
Simulating Sentiment ▪ The next goal: let machine have emotion ▪ We can read emotion from text now. ▪ How can we `mimic` the emotion? ▪ What is emotion? ▪ How to teach sentiment to machine? ▪ Sentiment Engine: ▪ Machine Learning Model to mimic emotional state and transitions
Teaching sentiment engine ▪ Characteristics of Emotion ▪ Memory effect: Affected by both short and long-range memory ▪ Flip-flop: can be reversed by small artifacts / events ▪ Mutuality: Both self and partner affects each other ▪ ML model structure requires: ▪ Memory ▪ Allows catastrophe ▪ On-demand input with self-sustained information RNN We will use To simulate sentimentality
Sentiment engine structure ▪ Input ▪ Prior emotion parameter sequences (p, n)i<t ▪ Current emotion parameter (pc , nc)t-1 of chatting partner (user) ▪ Output ▪ Emotion parameter (p, n)t ▪ Q) Avalanche? ▪ Hard problem. ▪ Why? Because we do not know why! ▪ Let’s simulate with a joke. (pc , nc)t-1 (p, n)i<t (p, n)t
Training sentiment engine ▪ Best point: it does not depend on language! ▪ I already made emotion reading machine (as you saw) ▪ My sentiment simulation engine requires sentiment parameters as input, not sentence. ▪ Scenario ▪ Train sentiment engine with English ▪ Use sentiment engine with Korean
Bonus: We need two of them. Why? For more interesting!
Double-identity chat-bot engines ▪ Why double identity? ▪ Bot talks to bot: Siri VS. Siri / Google Home talks to Google Home ▪ It’s funny! ▪ But is it worth for me? ▪ Do I need more bots than one? ▪ How can we make ‘multiple’ bots with same context? ▪ Same context with different opinion? ▪ Making one engine with dual identity is easier than running two engines.
…Still testing / working on the topic now.
One page summary The simplest is the best NOT SIMPLE ANYMORE T_T
Are you caring me now? You / Care / I / Now You / Look / Tired /Yesterday You looked tired yesterday. Ah, you looked very tired yesterday. [GUESS] I [CARE] [PRESENT] Disintegrator Context analyzer Decision maker Grammar generator Tone generator Lexical Output Sentence generator Deep-learning model (sentence-to-sentence + context-aware word generator) Grammar generator Context memory Knowledge engine Emotion engine Context parser Tone generator Disintegrator Response generator NLP + StV Context analyzer + Decision maker Lexical Input
Deep-learning model (sentence-to-sentence + context-aware word generator) Knowledge engine Emotion engine Context parser Context analyzer + Decision maker Emotion Reader Sentiment Engine Sentiment ML SentimentSpace Reader ML Context memory Zoom-in emotion ▪ Emotion Engine Summary ▪ Emotion Reader (ER) ▪ Sentiment Engine (SE) ▪ Sentiment ML: RNN for sentiment parameter estimation ▪ SentimentSpace ▪ WordVec space for ER / SE
With nostalgic tools (to someone) Demonstration
Data source ▪ Prototyping ▪ Idol master conversation script (translated by online fans) ▪ Field tests ▪ Animations only with female characters ▪ New data! ▪ Communication script from Idol master 2 / OFA ▪ Script from Idol master PS
Data converter (from PyCon APAC 2016!) .smi to .srt Join .srt files into one .txt Remove timestamps and blank lines Remove Logo / Ending Song scripts : Lines with Japanese Characters and the next lines of them Fetch Character names Nouns Numbers using custom dictionary (Anime characters, Locations, Specific nouns) cat *.srt >> data.txt subtitle_converter.py *.smi file format is de facto standard of movie caption files in Korea
Extract Conversations Conversation data for sequence-to-sequence Bot model Reformat merge sliced captions into one line if last_sentence [-1] == '?': conversation.add(( last_sentence, current_sentence)) Remove Too short sentences Duplicates Sentence data for disintegrator grammar model tone model Train disintegrator integrator with grammar model tone model Train bot model subtitle_converter.py Pandas + DBMS pandas https://github.com/inureyes/pycon-kr-2017-demo-nanika
Simplification for demo ▪ No context engine: simple seq2seq-based chat-bot model ▪ github.com/tensorflow/models/tutorials/rnn/translate ▪ No knowledge engine ▪ Labeled data for training sentiment engine ▪ Target parameter values are generated from response sentence ▪ Unreal: It is not recommended. ▪ Far from realistic mimicking ▪ In fact, SE is not needed in this case. (but this is demo)
…with support from backbone machine via internet through lablup.AI PaaS.
2017-08-11 14:44:17.817210: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.2017-08-11 14:44:17.817250: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.2017-08-11 14:44:17.817262: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.2017-08-11 14:44:17.817271: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.2017-08-11 14:44:17.817280: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.2017-08-11 14:44:18.826396: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:893] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero2017-08-11 14:44:18.827380: I tensorflow/core/common_runtime/gpu/gpu_device.cc:940] Found device 0 with properties:name: Tesla K80major: 3 minor: 7 memoryClockRate (GHz) 0.8235pciBusID 0000:00:17.0Total memory: 11.17GiBFree memory: 11.11GiB … 2017-08-11 14:44:19.804221: I tensorflow/core/common_runtime/gpu/gpu_device.cc:940] Found device 7 with properties:name: Tesla K80major: 3 minor: 7 memoryClockRate (GHz) 0.8235pciBusID 0000:00:1e.0Total memory: 11.17GiBFree memory: 11.11GiB 2017-08-11 14:44:19.835747: I tensorflow/core/common_runtime/gpu/gpu_device.cc:961] DMA: 0 1 2 3 4 5 6 7 2017-08-11 14:44:19.835769: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0: Y Y Y Y Y Y Y Y 2017-08-11 14:44:19.835775: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 1: Y Y Y Y Y Y Y Y 2017-08-11 14:44:19.835781: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 2: Y Y Y Y Y Y Y Y 2017-08-11 14:44:19.835785: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 3: Y Y Y Y Y Y Y Y 2017-08-11 14:44:19.835790: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 4: Y Y Y Y Y Y Y Y 2017-08-11 14:44:19.835794: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 5: Y Y Y Y Y Y Y Y 2017-08-11 14:44:19.835800: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 6: Y Y Y Y Y Y Y Y 2017-08-11 14:44:19.835805: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 7: Y Y Y Y Y Y Y Y Bot training procedure (initialization)
There was a clip a.k.a. Office Assistant Note: It is NOT a joke. A long time ago, © clippit, Microsoft Office 97 It was not fun.
© Yotsuba&!, ASCII Mediaworks
Back to the basic
Back to the basic Go back to the past
You can make AI even with Windows! Windows! …Windows? © Retrobot / 영실업
If you were crazy about desktop eyecandy, ▪ SSP / 나니카 ▪ Compatible Baseware of 伺か ▪ 伺か: Desktop accessary for geeks in 2000s. http://ssp.shillest.net https://plaza.rakuten.co.jp/iiwisblog/diary/200602250000/
Baseware Ghost Makoto Image Resources Configurations Script engine Script data SSP structure / modules ▪ Makoto: Translating module ▪ Shiori: Script execution module ▪ Ghost: Shiori + Shell ShellShiori ▪ Structure ▪ Baseware (SSP): execution engine ▪ Shell: character image files / animations result
Poster * seanloh.wordpress.com/2013/07/03/ghost-in-the-shell-1995/
The problem ▪ How to connect my chatbot engine to SSP? ▪ Candidate 1: headline feature (webpage/RSS parser) to read answer from ML engine ▪ Rejected: Reading interval too slow. ▪ Candidate 2: make new plugin ▪ Candidate 3: Create ghost DLL ▪ Rejected: More than 10 years passed since I use Windows OS. ▪ So, I decide to write the pseudo-SSP presentation (shell) layer ▪ The problem: I decided it yesterday.
First with the head, and with the heart ▪ Making Web-based Shell for demonstration ▪ Starts at 18:00PM ▪ Could record demo at 1:30AM ▪ https://github.com/inureyes/pyco n-kr-2017-demo-nanika ▪ Will be public after this talk.
Ghost (Engine)Web-based SSP Shell Express.js Polymer 2 Python 3 TensorFlow 1.2 Node.js Browser CLI Polling REST API
Pseudo-SSP demonstration structure ▪ Shell ▪ Written in node.js ▪ Why not django-rest? ▪ Ghost ▪ Written in Python3 / TensorFlow ▪ Context: seq2seq ▪ Emotion: Sentimental Engine / Emotion reader
Demo time The end is near. (In many ways)
Summary ▪ Today ▪ Dive into SyntaxNet and DRAGNN ▪ Emotion reading procedure using SentiWordNet and deep learning ▪ Emotion simulating with Sentiment Engine ▪ My contributions / insight to you ▪ Dodging Korean-specific problems when using SyntaxNet ▪ My own emotion reading / simulation algorithm] ▪ Dig into communication with multiple chat-bots ▪ And heartbreaking demonstration!
And next... ▪ Public service to read emotion from conversation ▪ Emobot: it will read the emotional context changes ▪ Invite to telegram, slack, etc. ▪ Talkativot.ai ▪ Tired of request wave… ▪ Pipeline for garage bots ▪ Also as a service
–Bryce Courtenay's The Power of One “First with the head, then with the heart.”
github.com/lablup/talkativot And I prepare the shovel for you: …Maybe, from late this month?
Thank you for listening :) @inureyes jeongkyu.shin github.com/inureyes

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