no code implementations • EMNLP (insights) 2020 • Samuel Louvan, Bernardo Magnini
Although several works have addressed the role of data selection to improve transfer learning for various NLP tasks, there is no consensus about its real benefits and, more generally, there is a lack of shared practices on how it can be best applied.
1 code implementation • EMNLP 2021 • Rahmad Mahendra, Alham Fikri Aji, Samuel Louvan, Fahrurrozi Rahman, Clara Vania
The expert-annotated data is used exclusively as a test set.
no code implementations • COLING 2020 • Samuel Louvan, Bernardo Magnini
In recent years, fostered by deep learning technologies and by the high demand for conversational AI, various approaches have been proposed that address the capacity to elicit and understand user's needs in task-oriented dialogue systems.
no code implementations • PACLIC 2020 • Samuel Louvan, Bernardo Magnini
Neural-based models have achieved outstanding performance on slot filling and intent classification, when fairly large in-domain training data are available.
no code implementations • WS 2019 • Samuel Louvan, Bernardo Magnini
Slot filling is a core operation for utterance understanding in task-oriented dialogue systems.
1 code implementation • 12 Oct 2018 • Kemal Kurniawan, Samuel Louvan
Automatic text summarization is generally considered as a challenging task in the NLP community.
no code implementations • WS 2018 • Samuel Louvan, Bernardo Magnini
Slot filling is a crucial task in the Natural Language Understanding (NLU) component of a dialogue system.
no code implementations • WS 2018 • Fariz Ikhwantri, Samuel Louvan, Kemal Kurniawan, Bagas Abisena, Valdi Rachman, Alfan Farizki Wicaksono, Rahmad Mahendra
In this paper, we propose a Multi-Task Active Learning framework for Semantic Role Labeling with Entity Recognition (ER) as the auxiliary task to alleviate the need for extensive data and use additional information from ER to help SRL.
1 code implementation • WS 2018 • Kemal Kurniawan, Samuel Louvan
We report an empirical evaluation of neural sequence labeling models with character embedding to tackle NER task in Indonesian conversational texts.