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Incorporating Constituent Syntax for Coreference Resolution ...
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PPT: Parsimonious Parser Transfer for Unsupervised Cross-Lingual Adaptation ...
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Abstract:
Cross-lingual transfer is a leading technique for parsing low-resource languages in the absence of explicit supervision. Simple `direct transfer' of a learned model based on a multilingual input encoding has provided a strong benchmark. This paper presents a method for unsupervised cross-lingual transfer that improves over direct transfer systems by using their output as implicit supervision as part of self-training on unlabelled text in the target language. The method assumes minimal resources and provides maximal flexibility by (a) accepting any pre-trained arc-factored dependency parser; (b) assuming no access to source language data; (c) supporting both projective and non-projective parsing; and (d) supporting multi-source transfer. With English as the source language, we show significant improvements over state-of-the-art transfer models on both distant and nearby languages, despite our conceptually simpler approach. We provide analyses of the choice of source languages for multi-source transfer, and ... : Accepted at EACL 2021 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2101.11216 https://dx.doi.org/10.48550/arxiv.2101.11216
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As Easy as 1, 2, 3: Behavioural Testing of NMT Systems for Numerical Translation ...
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Putting words into the system's mouth: A targeted attack on neural machine translation using monolingual data poisoning ...
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It Is Not As Good As You Think! Evaluating Simultaneous Machine Translation on Interpretation Data ...
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Balancing out Bias: Achieving Fairness Through Training Reweighting ...
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ChEMU 2020: Natural Language Processing Methods Are Effective for Information Extraction From Chemical Patents
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In: Front Res Metr Anal (2021)
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Learning Coupled Policies for Simultaneous Machine Translation using Imitation Learning ...
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Please Mind the Root: Decoding Arborescences for Dependency Parsing
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Measuring the Similarity of Grammatical Gender Systems by Comparing Partitions
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Learning a Cost-Effective Annotation Policy for Question Answering
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Pareto Probing: Trading Off Accuracy for Complexity
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Speakers Fill Lexical Semantic Gaps with Context
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Exploring the Linear Subspace Hypothesis in Gender Bias Mitigation
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Intrinsic Probing through Dimension Selection
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text Generation
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In: Findings of the Association for Computational Linguistics: EMNLP 2020 (2020)
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Textual Data Augmentation for Efficient Active Learning on Tiny Datasets
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