3,403 research outputs found
Using machine-learning to assign function labels to parser output for Spanish
Data-driven grammatical function tag assignment has been studied for English using the Penn-II Treebank data. In this paper we address the question of whether such methods can be applied successfully to other languages and treebank resources. In addition to tag assignment accuracy
and f-scores we also present results of a task-based evaluation. We use three machine-learning methods to assign
Cast3LB function tags to sentences parsed with Bikel’s parser trained on the Cast3LB treebank. The best performing method, SVM, achieves an f-score of 86.87% on gold-standard trees and 66.67% on parser output - a statistically significant improvement of 6.74% over the baseline. In a
task-based evaluation we generate LFG functional-structures from the function tag-enriched trees. On this task we achive
an f-score of 75.67%, a statistically significant 3.4% improvement over the baseline
Grammatical Error Correction: A Survey of the State of the Art
Grammatical Error Correction (GEC) is the task of automatically detecting and
correcting errors in text. The task not only includes the correction of
grammatical errors, such as missing prepositions and mismatched subject-verb
agreement, but also orthographic and semantic errors, such as misspellings and
word choice errors respectively. The field has seen significant progress in the
last decade, motivated in part by a series of five shared tasks, which drove
the development of rule-based methods, statistical classifiers, statistical
machine translation, and finally neural machine translation systems which
represent the current dominant state of the art. In this survey paper, we
condense the field into a single article and first outline some of the
linguistic challenges of the task, introduce the most popular datasets that are
available to researchers (for both English and other languages), and summarise
the various methods and techniques that have been developed with a particular
focus on artificial error generation. We next describe the many different
approaches to evaluation as well as concerns surrounding metric reliability,
especially in relation to subjective human judgements, before concluding with
an overview of recent progress and suggestions for future work and remaining
challenges. We hope that this survey will serve as comprehensive resource for
researchers who are new to the field or who want to be kept apprised of recent
developments
Recommended from our members
Hybrid System Combination for Machine Translation: An Integration of Phrase-level and Sentences-level Combination Approaches
Given the wide range of successful statistical MT approaches that have emerged recently, it would be beneficial to take advantage of their individual strengths and avoid their individual weaknesses. Multi-Engine Machine Translation (MEMT) attempts to do so by either fusing the output of multiple translation engines or selecting the best translation among them, aiming to improve the overall translation quality. In this thesis, we propose to use the phrase or the sentence as our combination unit instead of the word; three new phrase-level models and one sentence-level model with novel features are proposed. This contrasts with the most popular system combination technique to date which relies on word-level confusion network decoding.
Among the three new phrase-level models, the first one utilizes source sentences and target translation hypotheses to learn hierarchical phrases -- phrases that contain subphrases (Chiang 2007). It then re-decodes the source sentences using the hierarchical phrases to combine the results of multiple MT systems. The other two models we propose view combination as a paraphrasing process and use paraphrasing rules. The paraphrasing rules are composed of either string-to-string paraphrases or hierarchical paraphrases, learned from monolingual word alignments between a selected best translation hypothesis and other hypotheses. Our experimental results show that all of the three phrase-level models give superior performance in BLEU compared with the best single translation engine. The two paraphrasing models outperform the re-decoding model and the confusion network baseline model.
The sentence-level model exploits more complex syntactic and semantic information than the phrase-level models. It uses consensus, argument alignment, a supertag-based structural language model and a syntactic error detector. We use our sentence-level model in two ways: the first selects a translated sentence from multiple MT systems as the best translation to serve as a backbone for paraphrasing process; the second makes the final decision among all fused translations generated by the phrase-level models and all translated sentences of multiple MT systems. We proposed two novel hybrid combination structures for the integration of phrase-level and sentence-level combination frameworks in order to utilize the advantages of both frameworks and provide a more diverse set of plausible fused translations to consider
Towards standardizing Korean Grammatical Error Correction: Datasets and Annotation
Research on Korean grammatical error correction (GEC) is limited compared to
other major languages such as English and Chinese. We attribute this
problematic circumstance to the lack of a carefully designed evaluation
benchmark for Korean. Thus, in this work, we first collect three datasets from
different sources (Kor-Lang8, Kor-Native, and Kor-Learner) to cover a wide
range of error types and annotate them using our newly proposed tool called
Korean Automatic Grammatical error Annotation System (KAGAS). KAGAS is a
carefully designed edit alignment & classification tool that considers the
nature of Korean on generating an alignment between a source sentence and a
target sentence, and identifies error types on each aligned edit. We also
present baseline models fine-tuned over our datasets. We show that the model
trained with our datasets significantly outperforms the public statistical GEC
system (Hanspell) on a wider range of error types, demonstrating the diversity
and usefulness of the datasets.Comment: Add affiliation and email addres
- …