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1
Identity-Based Patterns in Deep Convolutional Networks: Generative Adversarial Phonology and Reduplication ...
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2
Signed Coreference Resolution ...
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3
Backtranslation in Neural Morphological Inflection ...
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4
Rule-based Morphological Inflection Improves Neural Terminology Translation ...
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5
Translating Headers of Tabular Data: A Pilot Study of Schema Translation ...
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6
A Prototype Free/Open-Source Morphological Analyser and Generator for Sakha ...
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7
Automatic Error Type Annotation for Arabic ...
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8
Developing Conversational Data and Detection of Conversational Humor in Telugu ...
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9
Cross-document Event Identity via Dense Annotation ...
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10
Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning ...
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11
(Mis)alignment Between Stance Expressed in Social Media Data and Public Opinion Surveys ...
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12
Adversarial Regularization as Stackelberg Game: An Unrolled Optimization Approach ...
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13
Rewards with Negative Examples for Reinforced Topic-Focused Abstractive Summarization ...
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14
Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training ...
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15
Low-Resource Dialogue Summarization with Domain-Agnostic Multi-Source Pretraining ...
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16
HittER: Hierarchical Transformers for Knowledge Graph Embeddings ...
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17
Ara-Women-Hate: The first Arabic Hate Speech corpus regarding Women ...
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18
Detecting Gender Bias using Explainability ...
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19
HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.13/ Abstract: To capture the semantic graph structure from raw text, most existing summarization approaches are built on GNNs with a pre-trained model. However, these methods suffer from cumbersome procedures and inefficient computations for long-text documents. To mitigate these issues, this paper proposes HETFORMER, a Transformer-based pre-trained model with multi-granularity sparse attentions for long-text extractive summarization. Specifically, we model different types of semantic nodes in raw text as a potential heterogeneous graph and directly learn heterogeneous relationships (edges) among nodes by Transformer. Extensive experiments on both single- and multi-document summarization tasks show that HETFORMER achieves state-of-the-art performance in Rouge F1 while using less memory and fewer parameters. ...
Keyword: Computational Linguistics; Language Models; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Text Summarization
URL: https://underline.io/lecture/37574-hetformer-heterogeneous-transformer-with-sparse-attention-for-long-text-extractive-summarization
https://dx.doi.org/10.48448/za5a-2696
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20
Blindness to Modality Helps Entailment Graph Mining ...
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