Andrey Bout


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Template-based Approach to Zero-shot Intent Recognition
Dmitry Lamanov | Pavel Burnyshev | Katya Artemova | Valentin Malykh | Andrey Bout | Irina Piontkovskaya
Proceedings of the 15th International Conference on Natural Language Generation


Single Example Can Improve Zero-Shot Data Generation
Pavel Burnyshev | Valentin Malykh | Andrey Bout | Ekaterina Artemova | Irina Piontkovskaya
Proceedings of the 14th International Conference on Natural Language Generation

Sub-tasks of intent classification, such as robustness to distribution shift, adaptation to specific user groups and personalization, out-of-domain detection, require extensive and flexible datasets for experiments and evaluation. As collecting such datasets is time- and labor-consuming, we propose to use text generation methods to gather datasets. The generator should be trained to generate utterances that belong to the given intent. We explore two approaches to the generation of task-oriented utterances: in the zero-shot approach, the model is trained to generate utterances from seen intents and is further used to generate utterances for intents unseen during training. In the one-shot approach, the model is presented with a single utterance from a test intent. We perform a thorough automatic, and human evaluation of the intrinsic properties of two-generation approaches. The attributes of the generated data are close to original test sets, collected via crowd-sourcing.

InFoBERT: Zero-Shot Approach to Natural Language Understanding Using Contextualized Word Embedding
Pavel Burnyshev | Andrey Bout | Valentin Malykh | Irina Piontkovskaya
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

Natural language understanding is an important task in modern dialogue systems. It becomes more important with the rapid extension of the dialogue systems’ functionality. In this work, we present an approach to zero-shot transfer learning for the tasks of intent classification and slot-filling based on pre-trained language models. We use deep contextualized models feeding them with utterances and natural language descriptions of user intents to get text embeddings. These embeddings then used by a small neural network to produce predictions for intent and slot probabilities. This architecture achieves new state-of-the-art results in two zero-shot scenarios. One is a single language new skill adaptation and another one is a cross-lingual adaptation.