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Frustratingly Simple but Surprisingly Strong: Using Language-Independent Features for Zero-shot Cross-lingual Semantic Parsing ...
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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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Textual Data Augmentation for Efficient Active Learning on Tiny Datasets
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LitCrit: exploring intentions as a basis for automated feedback on Related Work.
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Unsupervised stance detection for arguments from consequences
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XCOPA: A multilingual dataset for causal commonsense reasoning
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From zero to hero: On the limitations of zero-shot language transfer with multilingual transformers
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Inference in an Approach to Discourse Anaphora
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In: North East Linguistics Society (2020)
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Penn Discourse Treebank Version 3.0
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Abstract:
*Introduction* Penn Discourse Treebank (PDTB) Version 3.0 is the third release in the Penn Discourse Treebank project, the goal of which is to annotate the Wall Street Journal (WSJ) section of Treebank-2 (LDC95T7) with discourse relations. Penn Discourse Treebank Version 2 (LDC2008T05) contains over 40,600 tokens of annotated relations. In Version 3, an additional 13,000 tokens were annotated, certain pairwise annotations were standardized, new senses were included and the corpus was subject to a series of consistency checks. Details concerning the development of PDTB Version 3.0 can be found in the documentation accompanying this release. Largely because the PDTB project was based on the idea that discourse relations are grounded in an identifiable set of explicit words or phrases (discourse connectives) or simply in the adjacency of two sentences, the PTDB has been used by many researchers in the natural language processing community and more recently, by researchers in psycholinguistics. It has also stimulated the development of similar resources in other languages and domains. *Data* Annotations are provided in the form of separate text files (standoff annotation) that are byte-indexed into the raw WSJ text files in Treebank-2. The raw WSJ files are also included in this release. All text files are plain text, encoded in UTF-8. This corpus contains two tools: (1) The Annotator, used for annotation and adjudication, and which can also be used for viewing the corpus; and (2) The Conversion Tool for converting Version 2 annotation files into the Version 3 format. The documentation directory contains a manual describing what is new in Version 3 and how Version 3 differs from Version 2; the methods and guidelines used in annotating PDTB Version 3; and a range of statistics on the tokens, including the frequency of each connective, its sense labels and its modifiers. More information about the corpus and research carried out by the developers and others using the corpus can be found on the PDTB website. *Samples* One can see samples of the annotation of different types of discourse relations, along with their visualization in the Annotator tool at: * Explicit relations * Implicit relations * Altlex and AltLexC relations * Entity relations * Hypophora relations * NoRel (annotated only between adjacent sentences within a paragraph that are not linked to each other by a discourse relation) *Updates* Experiments carried out in Fall 2019 on the intra-sentential discourse relations in the PDTB-3 revealed two problems with the corpus: (1) the final versions of two gold files of "to clause" annotation had not been loaded, and (2) several tokens were inadvertently omitted on the assumption that they were duplicates, when they were not. Repairing these errors, and correcting a mis-labelled token in file wsj_1026, has added another 45 implicit intra-sentential relations to the corpus. Counts in the Annotation Manual have been adjusted to take these additional tokens into account. Specific changes/additions are recorded in the file "pdtb3-revision-jan-2020.txt". Downloads after February 3, 2020 contain the updated corpus. *Acknowledgment* This work has been funded by the National Science Foundation, under grant NSF IIS 1422186 to the University of Pennsylvania and grant NSF IIS 1421067 to the University of Wisconsin, Milwaukee. The content of this publication does not necessarily reflect the position or policy of the Government, and no official endorsement should be inferred.
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URL: https://catalog.ldc.upenn.edu/LDC2019T05
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A survey of cross-lingual features for zero-shot cross-lingual semantic parsing ...
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