DeepPavlov: Open-Source Library for Dialogue Systems
Mikhail Burtsev | Alexander Seliverstov | Rafael Airapetyan | Mikhail Arkhipov | Dilyara Baymurzina | Nickolay Bushkov | Olga Gureenkova | Taras Khakhulin | Yuri Kuratov | Denis Kuznetsov | Alexey Litinsky | Varvara Logacheva | Alexey Lymar | Valentin Malykh | Maxim Petrov | Vadim Polulyakh | Leonid Pugachev | Alexey Sorokin | Maria Vikhreva | Marat Zaynutdinov
Proceedings of ACL 2018, System Demonstrations
Adoption of messaging communication and voice assistants has grown rapidly in the last years. This creates a demand for tools that speed up prototyping of feature-rich dialogue systems. An open-source library DeepPavlov is tailored for development of conversational agents. The library prioritises efficiency, modularity, and extensibility with the goal to make it easier to develop dialogue systems from scratch and with limited data available. It supports modular as well as end-to-end approaches to implementation of conversational agents. Conversational agent consists of skills and every skill can be decomposed into components. Components are usually models which solve typical NLP tasks such as intent classification, named entity recognition or pre-trained word vectors. Sequence-to-sequence chit-chat skill, question answering skill or task-oriented skill can be assembled from components provided in the library.
Robust Word Vectors: Context-Informed Embeddings for Noisy Texts
Valentin Malykh | Varvara Logacheva | Taras Khakhulin
Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text
We suggest a new language-independent architecture of robust word vectors (RoVe). It is designed to alleviate the issue of typos, which are common in almost any user-generated content, and hinder automatic text processing. Our model is morphologically motivated, which allows it to deal with unseen word forms in morphologically rich languages. We present the results on a number of Natural Language Processing (NLP) tasks and languages for the variety of related architectures and show that proposed architecture is typo-proof.