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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 ...
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19
HittER: Hierarchical Transformers for Knowledge Graph Embeddings ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.812/ Abstract: This paper examines the challenging problem of learning representations of entities and relations in a complex multi-relational knowledge graph. We propose HittER, a Hierarchical Transformer model to jointly learn Entity-relation composition and Relational contextualization based on a source entity's neighborhood. Our proposed model consists of two different Transformer blocks: the bottom block extracts features of each entity-relation pair in the local neighborhood of the source entity and the top block aggregates the relational information from outputs of the bottom block. We further design a masked entity prediction task to balance information from the relational context and the source entity itself. Experimental results show that HittER achieves new state-of-the-art results on multiple link prediction datasets. We additionally propose a simple approach to integrate HittER into BERT and demonstrate its effectiveness on two ...
Keyword: Computational Linguistics; Information Extraction; Language Models; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
URL: https://dx.doi.org/10.48448/y853-5214
https://underline.io/lecture/37925-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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