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1
Multi-National Topics Maps for Parliamentary Debate Analysis
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2
Assessing English language sentences readability using machine learning models
In: PeerJ Comput Sci (2022)
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3
Identity-Based Patterns in Deep Convolutional Networks: Generative Adversarial Phonology and Reduplication ...
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4
G-4 - A pipeline for Hand 2-D Keypoint Localization using Unpaired Image to Image Translation ...
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5
Signed Coreference Resolution ...
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6
Backtranslation in Neural Morphological Inflection ...
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7
Rule-based Morphological Inflection Improves Neural Terminology Translation ...
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8
Translating Headers of Tabular Data: A Pilot Study of Schema Translation ...
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9
A Prototype Free/Open-Source Morphological Analyser and Generator for Sakha ...
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10
Automatic Error Type Annotation for Arabic ...
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11
Developing Conversational Data and Detection of Conversational Humor in Telugu ...
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12
Cross-document Event Identity via Dense Annotation ...
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13
Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning ...
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14
(Mis)alignment Between Stance Expressed in Social Media Data and Public Opinion Surveys ...
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15
Adversarial Regularization as Stackelberg Game: An Unrolled Optimization Approach ...
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16
Rewards with Negative Examples for Reinforced Topic-Focused Abstractive Summarization ...
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17
Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training ...
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18
Low-Resource Dialogue Summarization with Domain-Agnostic Multi-Source Pretraining ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.7/ Abstract: With the rapid increase in the volume of dialogue data from daily life, there is a growing demand for dialogue summarization. Unfortunately, training a large summarization model is generally infeasible due to the inadequacy of dialogue data with annotated summaries. Most existing works for low-resource dialogue summarization directly pretrain models in other domains, e.g., the news domain, but they generally neglect the huge difference between dialogues and conventional articles. To bridge the gap between out-of-domain pretraining and in-domain fine-tuning, in this work, we propose a multi-source pretraining paradigm to better leverage the external summary data. Specifically, we exploit large-scale in-domain non-summary data to separately pretrain the dialogue encoder and the summary decoder. The combined encoder-decoder model is then pretrained on the out-of-domain summary data using adversarial critics, aiming to facilitate ...
Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Text Summarization
URL: https://underline.io/lecture/37345-low-resource-dialogue-summarization-with-domain-agnostic-multi-source-pretraining
https://dx.doi.org/10.48448/zqjg-p406
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19
HittER: Hierarchical Transformers for Knowledge Graph Embeddings ...
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20
Ara-Women-Hate: The first Arabic Hate Speech corpus regarding Women ...
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