373 research outputs found
HamleDT 2.0: Thirty Dependency Treebanks Stanfordized
We present HamleDT 2.0 (HArmonized Multi-LanguagE Dependency Treebank). HamleDT 2.0 is a collection of 30 existing treebanks harmonized into a common annotation style, the Prague Dependencies, and further transformed into Stanford Dependencies, a treebank annotation style that became popular recently.
We use the newest basic Universal Stanford Dependencies, without added language-specific subtypes. We describe both of the annotation styles, including adjustments that were necessary to make, and provide details about the conversion process. We also discuss the differences between the two styles, evaluating their advantages and disadvantages, and note the effects of the differences on the conversion.
We regard the stanfordization as generally successful, although we admit several shortcomings, especially in the distinction between direct and indirect objects, that have to be addressed in future.
We release part of HamleDT 2.0 freely; we are not allowed to redistribute the whole dataset, but we do provide the conversion pipeline
Translation of "It" in a Deep Syntax Framework
We present a novel approach to the translation of the English personal pronoun it to Czech. We conduct a linguistic analysis on how the distinct categories of it are usually mapped to their Czech counterparts. Armed with these observations, we design a discriminative translation model of it, which is then integrated into the TectoMT deep syntax MT framework. Features in the model take advantage of rich syntactic annotation TectoMT is based on, external
tools for anaphoricity resolution, lexical co-occurrence frequencies measured on a large parallel corpus and gold coreference annotation. Even though the new model for it exhibits no improvement in terms of BLEU, manual evaluation shows that it outperforms the original solution in
8.5% sentences containing it
Cross-lingual Coreference Resolution of Pronouns
This work is, to our knowledge, a first attempt at a machine learning approach to cross-lingual
coreference resolution, i.e. coreference resolution (CR) performed on a bitext. Focusing on CR of English pronouns, we leverage language differences and enrich the feature set of a standard monolingual CR system for English with features extracted from the Czech side of the bitext. Our work also includes a supervised pronoun aligner that outperforms a GIZA++ baseline in terms of both intrinsic evaluation and evaluation on CR. The final cross-lingual CR system has successfully outperformed both a monolingual CR and a cross-lingual projection system
Proceedings
Proceedings of the Workshop on Annotation and
Exploitation of Parallel Corpora AEPC 2010.
Editors: Lars Ahrenberg, Jörg Tiedemann and Martin Volk.
NEALT Proceedings Series, Vol. 10 (2010), 98 pages.
© 2010 The editors and contributors.
Published by
Northern European Association for Language
Technology (NEALT)
http://omilia.uio.no/nealt .
Electronically published at
Tartu University Library (Estonia)
http://hdl.handle.net/10062/15893
Findings of the Shared Task on Multilingual Coreference Resolution
This paper presents an overview of the shared task on multilingual
coreference resolution associated with the CRAC 2022 workshop. Shared task
participants were supposed to develop trainable systems capable of identifying
mentions and clustering them according to identity coreference. The public
edition of CorefUD 1.0, which contains 13 datasets for 10 languages, was used
as the source of training and evaluation data. The CoNLL score used in previous
coreference-oriented shared tasks was used as the main evaluation metric. There
were 8 coreference prediction systems submitted by 5 participating teams; in
addition, there was a competitive Transformer-based baseline system provided by
the organizers at the beginning of the shared task. The winner system
outperformed the baseline by 12 percentage points (in terms of the CoNLL scores
averaged across all datasets for individual languages)
Proceedings
Proceedings of the Ninth International Workshop
on Treebanks and Linguistic Theories.
Editors: Markus Dickinson, Kaili Müürisep and Marco Passarotti.
NEALT Proceedings Series, Vol. 9 (2010), 268 pages.
© 2010 The editors and contributors.
Published by
Northern European Association for Language
Technology (NEALT)
http://omilia.uio.no/nealt .
Electronically published at
Tartu University Library (Estonia)
http://hdl.handle.net/10062/15891
Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP
Transfer learning, particularly approaches that combine multi-task learning
with pre-trained contextualized embeddings and fine-tuning, have advanced the
field of Natural Language Processing tremendously in recent years. In this
paper we present MaChAmp, a toolkit for easy fine-tuning of contextualized
embeddings in multi-task settings. The benefits of MaChAmp are its flexible
configuration options, and the support of a variety of natural language
processing tasks in a uniform toolkit, from text classification and sequence
labeling to dependency parsing, masked language modeling, and text generation.Comment: https://machamp-nlp.github.io
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