358 research outputs found
New Treebank or Repurposed? On the Feasibility of Cross-Lingual Parsing of Romance Languages with Universal Dependencies
This is the final peer-reviewed manuscript that was accepted for publication in Natural Language Engineering. Changes resulting from the publishing process, such as editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document.[Abstract] This paper addresses the feasibility of cross-lingual parsing with Universal Dependencies (UD) between Romance languages, analyzing its performance when compared to the use of manually annotated resources of the target languages. Several experiments take into account factors such as the lexical distance between the source and target varieties, the impact of delexicalization, the combination of different source treebanks or the adaptation of resources to the target language, among others. The results of these evaluations show that the direct application of a parser from one Romance language to another reaches similar labeled attachment score (LAS) values to those obtained with a manual annotation of about 3,000 tokens in the target language, and unlabeled attachment score (UAS) results equivalent to the use of around 7,000 tokens, depending on the case. These numbers can noticeably increase by performing a focused selection of the source treebanks. Furthermore, the removal of the words in the training corpus (delexicalization) is not useful in most cases of cross-lingual parsing of Romance languages. The lessons learned with the performed experiments were used to build a new UD treebank for Galician, with 1,000 sentences manually corrected after an automatic cross-lingual annotation. Several evaluations in this new resource show that a cross-lingual parser built with the best combination and adaptation of the source treebanks performs better (77 percent LAS and 82 percent UAS) than using more than 16,000 (for LAS results) and more than 20,000 (UAS) manually labeled tokens of Galician.Ministerio de EconomĂa y Competitividad; FJCI-2014-22853Ministerio de EconomĂa y Competitividad; FFI2014-51978-C2-1-RMinisterio de EconomĂa y Competitividad; FFI2014-51978-C2-2-
Universal, Unsupervised (Rule-Based), Uncovered Sentiment Analysis
We present a novel unsupervised approach for multilingual sentiment analysis
driven by compositional syntax-based rules. On the one hand, we exploit some of
the main advantages of unsupervised algorithms: (1) the interpretability of
their output, in contrast with most supervised models, which behave as a black
box and (2) their robustness across different corpora and domains. On the other
hand, by introducing the concept of compositional operations and exploiting
syntactic information in the form of universal dependencies, we tackle one of
their main drawbacks: their rigidity on data that are structured differently
depending on the language concerned. Experiments show an improvement both over
existing unsupervised methods, and over state-of-the-art supervised models when
evaluating outside their corpus of origin. Experiments also show how the same
compositional operations can be shared across languages. The system is
available at http://www.grupolys.org/software/UUUSA/Comment: 19 pages, 5 Tables, 6 Figures. This is the authors version of a work
that was accepted for publication in Knowledge-Based System
Benchmarking zero-shot and few-shot approaches for tokenization, tagging, and dependency parsing of Tagalog text
The grammatical analysis of texts in any human language typically involves a
number of basic processing tasks, such as tokenization, morphological tagging,
and dependency parsing. State-of-the-art systems can achieve high accuracy on
these tasks for languages with large datasets, but yield poor results for
languages such as Tagalog which have little to no annotated data. To address
this issue for the Tagalog language, we investigate the use of auxiliary data
sources for creating task-specific models in the absence of annotated Tagalog
data. We also explore the use of word embeddings and data augmentation to
improve performance when only a small amount of annotated Tagalog data is
available. We show that these zero-shot and few-shot approaches yield
substantial improvements on grammatical analysis of both in-domain and
out-of-domain Tagalog text compared to state-of-the-art supervised baselines.Comment: To appear at PACLIC 2022. 10 pages, 2 figures, 4 table
One Model to Rule them all: Multitask and Multilingual Modelling for Lexical Analysis
When learning a new skill, you take advantage of your preexisting skills and
knowledge. For instance, if you are a skilled violinist, you will likely have
an easier time learning to play cello. Similarly, when learning a new language
you take advantage of the languages you already speak. For instance, if your
native language is Norwegian and you decide to learn Dutch, the lexical overlap
between these two languages will likely benefit your rate of language
acquisition. This thesis deals with the intersection of learning multiple tasks
and learning multiple languages in the context of Natural Language Processing
(NLP), which can be defined as the study of computational processing of human
language. Although these two types of learning may seem different on the
surface, we will see that they share many similarities.
The traditional approach in NLP is to consider a single task for a single
language at a time. However, recent advances allow for broadening this
approach, by considering data for multiple tasks and languages simultaneously.
This is an important approach to explore further as the key to improving the
reliability of NLP, especially for low-resource languages, is to take advantage
of all relevant data whenever possible. In doing so, the hope is that in the
long term, low-resource languages can benefit from the advances made in NLP
which are currently to a large extent reserved for high-resource languages.
This, in turn, may then have positive consequences for, e.g., language
preservation, as speakers of minority languages will have a lower degree of
pressure to using high-resource languages. In the short term, answering the
specific research questions posed should be of use to NLP researchers working
towards the same goal.Comment: PhD thesis, University of Groninge
New Treebank or Repurposed? On the Feasibility of Cross-Lingual Parsing of Romance Languages with Universal Dependencies
This is the final peer-reviewed manuscript that was accepted for publication in Natural Language Engineering. Changes resulting from the publishing process, such as editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document.[Abstract] This paper addresses the feasibility of cross-lingual parsing with Universal Dependencies (UD) between Romance languages, analyzing its performance when compared to the use of manually annotated resources of the target languages. Several experiments take into account factors such as the lexical distance between the source and target varieties, the impact of delexicalization, the combination of different source treebanks or the adaptation of resources to the target language, among others. The results of these evaluations show that the direct application of a parser from one Romance language to another reaches similar labeled attachment score (LAS) values to those obtained with a manual annotation of about 3,000 tokens in the target language, and unlabeled attachment score (UAS) results equivalent to the use of around 7,000 tokens, depending on the case. These numbers can noticeably increase by performing a focused selection of the source treebanks. Furthermore, the removal of the words in the training corpus (delexicalization) is not useful in most cases of cross-lingual parsing of Romance languages. The lessons learned with the performed experiments were used to build a new UD treebank for Galician, with 1,000 sentences manually corrected after an automatic cross-lingual annotation. Several evaluations in this new resource show that a cross-lingual parser built with the best combination and adaptation of the source treebanks performs better (77 percent LAS and 82 percent UAS) than using more than 16,000 (for LAS results) and more than 20,000 (UAS) manually labeled tokens of Galician.Ministerio de EconomĂa y Competitividad; FJCI-2014-22853Ministerio de EconomĂa y Competitividad; FFI2014-51978-C2-1-RMinisterio de EconomĂa y Competitividad; FFI2014-51978-C2-2-
Inducing Language-Agnostic Multilingual Representations
Cross-lingual representations have the potential to make NLP techniques
available to the vast majority of languages in the world. However, they
currently require large pretraining corpora or access to typologically similar
languages. In this work, we address these obstacles by removing language
identity signals from multilingual embeddings. We examine three approaches for
this: (i) re-aligning the vector spaces of target languages (all together) to a
pivot source language; (ii) removing language-specific means and variances,
which yields better discriminativeness of embeddings as a by-product; and (iii)
increasing input similarity across languages by removing morphological
contractions and sentence reordering. We evaluate on XNLI and reference-free MT
across 19 typologically diverse languages. Our findings expose the limitations
of these approaches -- unlike vector normalization, vector space re-alignment
and text normalization do not achieve consistent gains across encoders and
languages. Due to the approaches' additive effects, their combination decreases
the cross-lingual transfer gap by 8.9 points (m-BERT) and 18.2 points (XLM-R)
on average across all tasks and languages, however. Our code and models are
publicly available.Comment: *SEM2021 Camera Read
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