@inproceedings{burnyshev-etal-2021-infobert,
title = "{I}n{F}o{BERT}: Zero-Shot Approach to Natural Language Understanding Using Contextualized Word Embedding",
author = "Burnyshev, Pavel and
Bout, Andrey and
Malykh, Valentin and
Piontkovskaya, Irina",
editor = "Mitkov, Ruslan and
Angelova, Galia",
booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)",
month = sep,
year = "2021",
address = "Held Online",
publisher = "INCOMA Ltd.",
url = "https://aclanthology.org/2021.ranlp-1.25",
pages = "208--215",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T InFoBERT: Zero-Shot Approach to Natural Language Understanding Using Contextualized Word Embedding
%A Burnyshev, Pavel
%A Bout, Andrey
%A Malykh, Valentin
%A Piontkovskaya, Irina
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F burnyshev-etal-2021-infobert
%X 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.
%U https://aclanthology.org/2021.ranlp-1.25
%P 208-215
Markdown (Informal)
[InFoBERT: Zero-Shot Approach to Natural Language Understanding Using Contextualized Word Embedding](https://aclanthology.org/2021.ranlp-1.25) (Burnyshev et al., RANLP 2021)
ACL