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.
|Persons-1000 dataset with additional LOC and ORG markup||95.25|
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.
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.
|DSTC 2||DSTC 2 on DSTC 2 embeddings||28 intents||En||Accuracy||0.7732||0.7868|
|DSTC 2||DSTC 2 on Wiki embeddings||28 intents||En||Accuracy||0.9602||0.9593|
|SNIPS-2017||SNIPS on DSTC 2 embeddings||7 intents||En||F1||0.8664||–|
|SNIPS-2017||SNIPS on Wiki embeddings||7 intents||En||F1||0.9808||–|
|Insults||InsultsKaggle on Reddit embeddings||Insult detection||En||ROC-AUC||0.9271||0.8618|
|AG News||AG News on Wiki embeddings||5 topics||En||Accuracy||0.8876||0.9011|
|Twitter mokoron||Twitter on RuWiki+Lenta embeddings without any preprocessing||Sentiment||Ru||Accuracy||0.9972||0.9971|
|Twitter mokoron||Twitter on RuWiki+Lenta embeddings with preprocessing||Sentiment||Ru||Accuracy||0.7811||0.7749|
|RuSentiment||RuSentiment on RuWiki+Lenta embeddings||Sentiment||Ru||F1||0.6393||0.6539|
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  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.
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||0.5288||0.5248|
|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, 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.
|Correction method||Precision||Recall||F-measure||Speed (sentences/s)|
|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|
|Brill Moore top 1||41.29||37.26||39.17||2.4|
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 models for ranking:
|Dataset||Model config||Validation (Recall@1)||Test1 (Recall@1)|
Available pre-trained models for paraphrase identification:
|Dataset||Model config||Val (accuracy)||Test (accuracy)||Val (F1)||Test (F1)||Val (log_loss)||Test (log_loss)|
|Quora Question Pairs||paraphrase_ident_qqp||87.1||87.0||83.0||82.6||0.300||0.305|
|Quora Question Pairs||paraphrase_ident_qqp||87.7||87.5||84.0||83.8||0.287||0.298|
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|
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)|
|SDSJ Task B||squad_ru||60.62||80.04|
In the case when answer is not necessary present in given context we have squad_noans model. This model outputs empty string in case if there is no answer in context.
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. For more scores see full table.
|Dataset||Model||Word accuracy||Sent. accuracy|
|UD2.0 (Russian)||Pymorphy + russian_tagsets (first tag)||60.93||0.00|
|UD Pipe 1.2 (Straka et al., 2017)||93.57||43.04|
|UD2.0 (Czech)||UD Pipe 1.2 (Straka et al., 2017)||91.86||42.28|
|UD2.0 (English)||UD Pipe 1.2 (Straka et al., 2017)||92.89||55.75|
|UD2.0 (German)||UD Pipe 1.2 (Straka et al., 2017)||76.65||10.24|
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.
The eCommerce bot intends to retrieve product items from catalog in sorted order. In addition, it asks an user to provide additional information to specify the search.
An open domain question answering skill. The skill accepts free-form questions about the world and outputs an answer based on its Wikipedia knowledge.
|SQuAD (dev)||enwiki (2018-02-11)||28.0|
Hyperparameters optimization (either by cross-validation or neural evolution) for DeepPavlov models that requires only some small changes in a config file.
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.