Features

Components

Based on neural Named Entity Recognition network. The NER component reproduces architecture from the paper Application of a Hybrid Bi-LSTM-CRF model to the task of Russian Named Entity Recognition which is inspired by Bi-LSTM+CRF architecture from https://arxiv.org/pdf/1603.01360.pdf.

Dataset Test F1
Persons-1000 dataset with additional LOC and ORG markup 95.25
DSTC 2 98.40
OntoNotes 87.07

Based on fuzzy Levenshtein search to extract normalized slot values from text. The components either rely on NER results or perform needle in haystack search.

Dataset Slots Accuracy
DSTC 2 98.85

Component for classification tasks (intents, sentiment, etc) on word-level. Shallow-and-wide CNN, Deep CNN, BiLSTM, BiLSTM with self-attention and other models are presented. The model also allows multilabel classification of texts. Several pre-trained models are available and presented in Table below.

Dataset Model Task Lang Metric Valid Test
DSTC 2 DSTC 2 on DSTC 2 embeddings 28 intents En Accuracy 0.8554 0.8658
DSTC 2 DSTC 2 on Wiki embeddings 28 intents En Accuracy 0.9659 0.9659
SNIPS-2017 SNIPS on DSTC 2 embeddings 7 intents En F1 0.8821 –
SNIPS-2017 SNIPS on Wiki embeddings 7 intents En F1 0.9852 –
Insults InsultsKaggle on Reddit embeddings Insult detection En ROC-AUC 0.9287 0.8602
AG News AG News on Wiki embeddings 5 topics En Accuracy 0.8735 0.8859
Twitter mokoron Twitter on RuWiki+Lenta embeddings without any preprocessing Sentiment Ru Accuracy 0.9968 0.9971
Twitter mokoron Twitter on RuWiki+Lenta embeddings with preprocessing Sentiment Ru Accuracy 0.7944 0.7879
RuSentiment RuSentiment on RuWiki+Lenta embeddings Sentiment Ru F1 0.7843 0.6556

As no one had published intent recognition for DSTC-2 data, the comparison of the presented model is given on SNIPS dataset. The evaluation of model scores was conducted in the same way as in [3] to compare with the results from the report of the authors of the dataset. The results were achieved with tuning of parameters and embeddings trained on Reddit dataset.

Model AddToPlaylist BookRestaurant GetWheather PlayMusic RateBook SearchCreativeWork SearchScreeningEvent
api.ai 0.9931 0.9949 0.9935 0.9811 0.9992 0.9659 0.9801
ibm.watson 0.9931 0.9950 0.9950 0.9822 0.9996 0.9643 0.9750
microsoft.luis 0.9943 0.9935 0.9925 0.9815 0.9988 0.9620 0.9749
wit.ai 0.9877 0.9913 0.9921 0.9766 0.9977 0.9458 0.9673
snips.ai 0.9873 0.9921 0.9939 0.9729 0.9985 0.9455 0.9613
recast.ai 0.9894 0.9943 0.9910 0.9660 0.9981 0.9424 0.9539
amazon.lex 0.9930 0.9862 0.9825 0.9709 0.9981 0.9427 0.9581
               
Shallow-and-wide CNN 0.9956 0.9973 0.9968 0.9871 0.9998 0.9752 0.9854

Based on Hybrid Code Networks (HCNs) architecture from Jason D. Williams, Kavosh Asadi, Geoffrey Zweig, Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning – 2017. It allows to predict responses in goal-oriented dialog. The model is customizable: embeddings, slot filler and intent classifier can be switched on and off on demand.

Available pre-trained models:

Dataset & Model Valid turn accuracy Test turn accuracy
DSTC2, bot with slot filler & intents 0.5179 0.5125
DSTC2, bot with slot filler & embeddings & attention 0.5538 0.5551

Other benchmarks on DSTC2 (can’t be directly compared due to dataset modifications):

Dataset & Model Test turn accuracy
DSTC2, Bordes and Weston (2016) 0.411
DSTC2, Perez and Liu (2016) 0.487
DSTC2, Eric and Manning (2017) 0.480
DSTC2, Williams et al. (2017) 0.556

Dialogue agent predicts responses in a goal-oriented dialog and is able to handle multiple domains (pretrained bot allows calendar scheduling, weather information retrieval, and point-of-interest navigation). The model is end-to-end differentiable and does not need to explicitly model dialogue state or belief trackers.

Comparison of deeppavlov pretrained model with others:

Dataset & Model Valid BLEU Test BLEU
Kvret, KvretNet 0.1319 0.1328
Kvret, KvretNet, Mihail Eric et al. (2017) – 0.132
Kvret, CopyNet, Mihail Eric et al. (2017) – 0.110
Kvret, Attn Seq2Seq, Mihail Eric et al. (2017) – 0.102
Kvret, Rule-based, Mihail Eric et al. (2017) – 0.066

Pipelines that use candidates search in a static dictionary and an ARPA language model to correct spelling errors.

Compariosn on the test set for the SpellRuEval competition on Automatic Spelling Correction for Russian:

Correction method Precision Recall F-measure Speed (sentences/s)
Yandex.Speller 83.09 59.86 69.59
Damerau Levenshtein 1 + lm 53.26 53.74 53.50 29.3
Brill Moore top 4 + lm 51.92 53.94 52.91 0.6
Hunspell + lm 41.03 48.89 44.61 2.1
JamSpell 44.57 35.69 39.64 136.2
Brill Moore top 1 41.29 37.26 39.17 2.4
Hunspell 30.30 34.02 32.06 20.3

Based on LSTM-based deep learning models for non-factoid answer selection. The model performs ranking of responses or contexts from some database by their relevance for the given context.

Available pre-trained model(s):

Dataset Model config Validation (Recall@1) Test1 (Recall@1)
InsuranceQA V1 ranking_insurance 67.6 67.6

Comparison with other models on the InsuranceQA V1:

Model Validation (Recall@1) Test1 (Recall@1)
Architecture II (HLQA(200) CNNQA(4000) 1-MaxPooling Tanh) 61.8 62.8
QA-LSTM basic-model(max pooling) 64.3 63.1
ranking_insurance 67.6 67.6

Based on R-NET: Machine Reading Comprehension with Self-matching Networks. The model solves the task of looking for an answer on a question in a given context (SQuAD task format).

Dataset Model config EM (dev) F-1 (dev)
SQuAD-v1.1 squad 71.41 80.26
SDSJ Task B squad_ru 60.58 80.22

Based on character-based approach to morphological tagging Heigold et al., 2017. An extensive empirical evaluation of character-based morphological tagging for 14 languages. A state-of-the-art model for Russian and several other languages. Model takes as input tokenized sentences and outputs the corresponding sequence of morphological labels in UD format. The table below contains word and sentence accuracy on UD2.0 datasets.

Dataset Model Word accuracy Sent. accuracy
UD2.0 Russian UD Pipe 1.2 (Straka et al., 2017) 93.57 43.04
Basic model 95.17 50.58
Pymorphy-enhanced model 96.23 58.00

Set of pipelines for FAQ task: classifying incoming question into set of known questions and return prepared answer. You can build different pipelines based on: tf-idf, weighted fasttext, cosine similarity, logistic regression.

Skills

An open domain question answering skill. The skill accepts free-form questions about the world and outputs an answer based on its Wikipedia knowledge.

Dataset Wiki dump F1
SQuAD (dev) enwiki (2018-02-11) 28.0

Parameters evolution

Implementation of parameters evolution for DeepPavlov models that requires only some small changes in a config file.

Embeddings

Word vectors for the Russian language trained on joint Russian Wikipedia and Lenta.ru corpora.

Examples of some components

  • Run goal-oriented bot with Telegram interface:

    python -m deeppavlov interactbot deeppavlov/configs/go_bot/gobot_dstc2.json -d -t <TELEGRAM_TOKEN>

  • Run goal-oriented bot with console interface:

    python -m deeppavlov interact deeppavlov/configs/go_bot/gobot_dstc2.json -d

  • Run goal-oriented bot with REST API:

    python -m deeppavlov riseapi deeppavlov/configs/go_bot/gobot_dstc2.json -d

  • Run slot-filling model with Telegram interface:

    python -m deeppavlov interactbot deeppavlov/configs/ner/slotfill_dstc2.json -d -t <TELEGRAM_TOKEN>

  • Run slot-filling model with console interface:

    python -m deeppavlov interact deeppavlov/configs/ner/slotfill_dstc2.json -d

  • Run slot-filling model with REST API:

    python -m deeppavlov riseapi deeppavlov/configs/ner/slotfill_dstc2.json -d

  • Predict intents on every line in a file:

    python -m deeppavlov predict deeppavlov/configs/classifiers/intents_snips.json -d --batch-size 15 < /data/in.txt > /data/out.txt

View video demo of deployment of a goal-oriented bot and a slot-filling model with Telegram UI.