Search Results for author: Samuel Louvan

Found 10 papers, 3 papers with code

How Far Can We Go with Data Selection? A Case Study on Semantic Sequence Tagging Tasks

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.

Multi-Task Learning

Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey

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.

intent-classification Intent Classification +5

Simple is Better! Lightweight Data Augmentation for Low Resource Slot Filling and Intent Classification

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.

Data Augmentation General Classification +5

IndoSum: A New Benchmark Dataset for Indonesian Text Summarization

1 code implementation12 Oct 2018 Kemal Kurniawan, Samuel Louvan

Automatic text summarization is generally considered as a challenging task in the NLP community.

Extractive Summarization Text Summarization

Multi-Task Active Learning for Neural Semantic Role Labeling on Low Resource Conversational Corpus

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.

Active Learning Multi-Task Learning +1

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