no code implementations • INLG (ACL) 2021 • Pavel Burnyshev, Valentin Malykh, Andrey Bout, Ekaterina Artemova, Irina Piontkovskaya
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.
no code implementations • RANLP 2021 • Pavel Burnyshev, Andrey Bout, Valentin Malykh, Irina Piontkovskaya
Natural language understanding is an important task in modern dialogue systems.
no code implementations • 15 Oct 2024 • Konstantin Yakovlev, Sergey Nikolenko, Andrey Bout
The recently proposed ToolkenGPT tool learning paradigm demonstrates promising performance but suffers from two major issues: first, it cannot benefit from tool documentation, and second, it often makes mistakes in whether to use a tool at all.
no code implementations • 20 Nov 2023 • Andrey Bout, Alexander Podolskiy, Sergey Nikolenko, Irina Piontkovskaya
Progress in neural grammatical error correction (GEC) is hindered by the lack of annotated training data.
1 code implementation • 14 Nov 2023 • Konstantin Yakovlev, Alexander Podolskiy, Andrey Bout, Sergey Nikolenko, Irina Piontkovskaya
Grammatical error correction (GEC) is an important NLP task that is currently usually solved with autoregressive sequence-to-sequence models.
no code implementations • 14 Nov 2023 • Konstantin Yakovlev, Gregory Polyakov, Ilseyar Alimova, Alexander Podolskiy, Andrey Bout, Sergey Nikolenko, Irina Piontkovskaya
A recent trend in multimodal retrieval is related to postprocessing test set results via the dual-softmax loss (DSL).
no code implementations • 20 Mar 2023 • Xiaozhe Ren, Pingyi Zhou, Xinfan Meng, Xinjing Huang, Yadao Wang, Weichao Wang, Pengfei Li, Xiaoda Zhang, Alexander Podolskiy, Grigory Arshinov, Andrey Bout, Irina Piontkovskaya, Jiansheng Wei, Xin Jiang, Teng Su, Qun Liu, Jun Yao
In this work, we develop a system that trained a trillion-parameter language model on a cluster of Ascend 910 AI processors and MindSpore framework, and present the language model with 1. 085T parameters named PanGu-{\Sigma}.
no code implementations • 22 Jun 2022 • Dmitry Lamanov, Pavel Burnyshev, Ekaterina Artemova, Valentin Malykh, Andrey Bout, Irina Piontkovskaya
We outperform previous state-of-the-art f1-measure by up to 16\% for unseen intents, using intent labels and user utterances and without accessing external sources (such as knowledge bases).
no code implementations • 16 Aug 2021 • Pavel Burnyshev, Valentin Malykh, Andrey Bout, Ekaterina Artemova, Irina Piontkovskaya
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.
1 code implementation • 11 Jan 2021 • Alexander Podolskiy, Dmitry Lipin, Andrey Bout, Ekaterina Artemova, Irina Piontkovskaya
In turn, the Mahalanobis distance captures this disparity easily.