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How does the pre-training objective affect what large language models learn about linguistic properties? ...
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Automatic Identification and Classification of Bragging in Social Media ...
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3
Analyzing Online Political Advertisements ...
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4
Modeling the Severity of Complaints in Social Media ...
Jin, Mali; Aletras, Nikolaos. - : arXiv, 2021
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5
Translation Error Detection as Rationale Extraction ...
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6
Knowledge Distillation for Quality Estimation ...
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7
Frustratingly Simple Pretraining Alternatives to Masked Language Modeling ...
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8
Analyzing Online Political Advertisements ...
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9
Improving the Faithfulness of Attention-based Explanations with Task-specific Information for Text Classification ...
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10
Enjoy the Salience: Towards Better Transformer-based Faithful Explanations with Word Salience ...
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11
Modeling the Severity of Complaints in Social Media ...
NAACL 2021 2021; Aletras, Nikolaos; Jin, Mali. - : Underline Science Inc., 2021
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12
Active Learning by Acquiring Contrastive Examples ...
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13
In Factuality: Efficient Integration of Relevant Facts for Visual Question Answering ...
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14
Frustratingly Simple Pretraining Alternatives to Masked Language Modeling ...
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15
Knowledge Distillation for Quality Estimation ...
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16
Machine Extraction of Tax Laws from Legislative Texts
In: Proceedings of the Natural Legal Language Processing Workshop 2021 (2021)
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17
Point-of-Interest Type Prediction using Text and Images ...
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18
Point-of-Interest Type Prediction using Text and Images ...
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19
An Empirical Study on Leveraging Position Embeddings for Target-oriented Opinion Words Extraction ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.722/ Abstract: Target-oriented opinion words extraction (TOWE) (Fan et al., 2019b) is a new subtask of target-oriented sentiment analysis that aims to extract opinion words for a given aspect in text. Current state-of-the-art methods leverage position embeddings to capture the relative position of a word to the target. However, the performance of these methods depends on the ability to incorporate this information into word representations. In this paper, we explore a variety of text encoders based on pretrained word embeddings or language models that leverage part-of-speech and position embeddings, aiming to examine the actual contribution of each component in TOWE. We also adapt a graph convolutional network (GCN) to enhance word representations by incorporating syntactic information. Our experimental results demonstrate that BiLSTM-based models can effectively encode position information into word representations while using a GCN only ...
Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
URL: https://dx.doi.org/10.48448/gvhf-5432
https://underline.io/lecture/37375-an-empirical-study-on-leveraging-position-embeddings-for-target-oriented-opinion-words-extraction
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20
Knowledge distillation for quality estimation
Gajbhiye, Amit; Fomicheva, Marina; Alva-Manchego, Fernando. - : Association for Computational Linguistics, 2021
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