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Le modèle Transformer: un « couteau suisse » pour le traitement automatique des langues
In: Techniques de l'Ingenieur ; https://hal.archives-ouvertes.fr/hal-03619077 ; Techniques de l'Ingenieur, Techniques de l'ingénieur, 2022, ⟨10.51257/a-v1-in195⟩ ; https://www.techniques-ingenieur.fr/base-documentaire/innovation-th10/innovations-en-electronique-et-tic-42257210/transformer-des-reseaux-de-neurones-pour-le-traitement-automatique-des-langues-in195/ (2022)
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2
On Homophony and Rényi Entropy ...
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3
On Multi-domain Sentence Level Sentiment Analysis for Roman Urdu ...
Mehmood, Khawar. - : UNSW Sydney, 2021
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4
Generative Imagination Elevates Machine Translation ...
NAACL 2021 2021; Li, Lei; Long, Quanyu. - : Underline Science Inc., 2021
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5
Multilingual Email Zoning - Segmenting Multilingual Email Text Into Zones
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6
Contextualised sentiment analysis in the financial domain
Daudert, Tobias. - : NUI Galway, 2021
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7
Identity-Based Patterns in Deep Convolutional Networks: Generative Adversarial Phonology and Reduplication ...
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8
Signed Coreference Resolution ...
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9
Backtranslation in Neural Morphological Inflection ...
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10
Rule-based Morphological Inflection Improves Neural Terminology Translation ...
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11
Translating Headers of Tabular Data: A Pilot Study of Schema Translation ...
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12
A Prototype Free/Open-Source Morphological Analyser and Generator for Sakha ...
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13
Automatic Error Type Annotation for Arabic ...
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14
Developing Conversational Data and Detection of Conversational Humor in Telugu ...
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15
An Information-Theoretic Characterization of Morphological Fusion ...
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16
Cross-document Event Identity via Dense Annotation ...
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17
Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning ...
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18
(Mis)alignment Between Stance Expressed in Social Media Data and Public Opinion Surveys ...
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19
Adversarial Regularization as Stackelberg Game: An Unrolled Optimization Approach ...
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20
Rewards with Negative Examples for Reinforced Topic-Focused Abstractive Summarization ...
Abstract: We consider the problem of topic-focused ab- stractive summarization, where the goal is to generate an abstractive summary focused on a particular topic, a phrase of one or multiple words. We hypothesize that the task of generating topic-focused summaries can be improved by showing the model what it must not focus on. We introduce a deep reinforcement learning approach to topic-focused abstractive summarization, trained on rewards with a novel negative example baseline. We define the input in this problem as the source text preceded by the topic. We adapt the CNN-Daily Mail and New York Times summarization datasets for this task. We then show through experiments on existing rewards that the use of a negative example baseline can outperform the use of a self-critical baseline, in ROUGE, BERTSCORE, and human evaluation metrics. ...
Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Text Summarization
URL: https://underline.io/lecture/39831-rewards-with-negative-examples-for-reinforced-topic-focused-abstractive-summarization
https://dx.doi.org/10.48448/xfsb-0734
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