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SEAGLE: A platform for comparative evaluation of semantic encoders for information retrieval
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63 |
Specializing distributional vectors of all words for lexical entailment
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64 |
How to (properly) evaluate cross-lingual word embeddings: On strong baselines, comparative analyses, and some misconceptions
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65 |
Cross-lingual semantic specialization via lexical relation induction
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66 |
Generalized tuning of distributional word vectors for monolingual and cross-lingual lexical entailment
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67 |
SenZi: A sentiment analysis lexicon for the latinised Arabic (Arabizi)
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68 |
Informing unsupervised pretraining with external linguistic knowledge
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69 |
Do we really need fully unsupervised cross-lingual embeddings?
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70 |
Are we consistently biased? Multidimensional analysis of biases in distributional word vectors
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71 |
Unsupervised Cross-Lingual Information Retrieval using Monolingual Data Only ...
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72 |
Unsupervised Cross-Lingual Information Retrieval Using Monolingual Data Only ...
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73 |
Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization ...
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74 |
Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ...
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75 |
A Resource-Light Method for Cross-Lingual Semantic Textual Similarity ...
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76 |
Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ...
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77 |
Unsupervised Cross-Lingual Information Retrieval Using Monolingual Data Only
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78 |
ArguminSci: a tool for analyzing argumentation and rhetorical aspects in scientific writing
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80 |
Investigating the role of argumentation in the rhetorical analysis of scientific publications with neural multi-task learning models
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Abstract:
Exponential growth in the number of scientific publications yields the need for effective automatic analysis of rhetorical aspects of scientific writing. Acknowledging the argumentative nature of scientific text, in this work we investigate the link between the argumentative structure of scientific publications and rhetorical aspects such as discourse categories or citation contexts. To this end, we (1) augment a corpus of scientific publications annotated with four layers of rhetoric annotations with argumentation annotations and (2) investigate neural multi-task learning architectures combining argument extraction with a set of rhetorical classification tasks. By coupling rhetorical classifiers with the extraction of argumentative components in a joint multi-task learning setting, we obtain significant performance gains for different rhetorical analysis tasks.
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Keyword:
004 Informatik
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URL: https://madoc.bib.uni-mannheim.de/46086/ https://madoc.bib.uni-mannheim.de/46086/1/emnlp-18-multi%20%2831%29.pdf https://madoc.bib.uni-mannheim.de/46086
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