714 research outputs found
Multilingual Name Entity Recognition and Intent Classification Employing Deep Learning Architectures
Named Entity Recognition and Intent Classification are among the most
important subfields of the field of Natural Language Processing. Recent
research has lead to the development of faster, more sophisticated and
efficient models to tackle the problems posed by those two tasks. In this work
we explore the effectiveness of two separate families of Deep Learning networks
for those tasks: Bidirectional Long Short-Term networks and Transformer-based
networks. The models were trained and tested on the ATIS benchmark dataset for
both English and Greek languages. The purpose of this paper is to present a
comparative study of the two groups of networks for both languages and showcase
the results of our experiments. The models, being the current state-of-the-art,
yielded impressive results and achieved high performance.Comment: 24 pages, 5 figures, 11 tables, dataset availabl
Fine-Tuning BERT Models for Intent Recognition Using a Frequency Cut-Off Strategy for Domain-Specific Vocabulary Extension
The work leading to these results was supported by the Spanish Ministry of Science and Innovation through the R& D&i projects GOMINOLA (PID2020-118112RB-C21 and PID2020118112RB-C22, funded by MCIN/AEI/10.13039/501100011033), CAVIAR (TEC2017-84593-C2-1-R, funded by MCIN/ AEI/10.13039/501100011033/FEDER "Una manera de hacer Europa"), and AMICPoC (PDC2021-120846-C42, funded by MCIN/AEI/10.13039/501100011033 and by "the European Union "NextGenerationEU/PRTR"). This research also received funding from the European Union's Horizon2020 research and innovation program under grant agreement No 823907 (http://menhirproject.eu, accessed on 2 February 2022). Furthermore, R.K.'s research was supported by the Spanish Ministry of Education (FPI grant PRE2018-083225).Intent recognition is a key component of any task-oriented conversational system. The
intent recognizer can be used first to classify the user’s utterance into one of several predefined classes
(intents) that help to understand the user’s current goal. Then, the most adequate response can be
provided accordingly. Intent recognizers also often appear as a form of joint models for performing
the natural language understanding and dialog management tasks together as a single process, thus
simplifying the set of problems that a conversational system must solve. This happens to be especially
true for frequently asked question (FAQ) conversational systems. In this work, we first present an
exploratory analysis in which different deep learning (DL) models for intent detection and classification
were evaluated. In particular, we experimentally compare and analyze conventional recurrent
neural networks (RNN) and state-of-the-art transformer models. Our experiments confirmed that
best performance is achieved by using transformers. Specifically, best performance was achieved by
fine-tuning the so-called BETO model (a Spanish pretrained bidirectional encoder representations
from transformers (BERT) model from the Universidad de Chile) in our intent detection task. Then, as
the main contribution of the paper, we analyze the effect of inserting unseen domain words to extend
the vocabulary of the model as part of the fine-tuning or domain-adaptation process. Particularly,
a very simple word frequency cut-off strategy is experimentally shown to be a suitable method for
driving the vocabulary learning decisions over unseen words. The results of our analysis show that
the proposed method helps to effectively extend the original vocabulary of the pretrained models.
We validated our approach with a selection of the corpus acquired with the Hispabot-Covid19 system
obtaining satisfactory results.Spanish Ministry of Science and Innovation (MCIN/AEI) PID2020-118112RB-C21
PID2020118112RB-C22
PDC2021-120846-C42Spanish Ministry of Science and Innovation (MCIN/AEI/FEDER "Una manera de hacer Europa") TEC2017-84593-C2-1-RSpanish Ministry of Science and Innovation (European Union "NextGenerationEU/PRTR") PDC2021-120846-C42European Commission 823907German Research Foundation (DFG) PRE2018-08322
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