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Register a new userParadigm Clustering with Weighted Edit Distance
تجميع النموذج مع مسافة تحرير المرجح
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تصف هذه الورقة نظامنا لمهمة SIGMORPHON 2021 المشتركة على تجميع النموذج المورفولوجي غير المقترح، والذي يطلب من المشاركين أن يثبتوا نماذج تم تأصيلهم معا وفقا ليمما الأساسي دون مساعدة من بيانات التدريب المشروح.نحن نوظف تجميع التجمعات التجمعات إلى مجموعة الكلمة معا باستخدام مقياس تقيي يجمع بين مسافة إلكترونية ومن المسافة الدلالية من Word Ageddings.نقوم بتجربة اثنين من الاختلافات في تحرير النموذج المستند إليه عن بعد لقياس المسافة الإلكترونية، ولكن نظرا لقيود الوقت، لا يتحسن نظامنا عبر النظام الأساسي للمهمة المشتركة.
This paper describes our system for the SIGMORPHON 2021 Shared Task on Unsupervised Morphological Paradigm Clustering, which asks participants to group inflected forms together according their underlying lemma without the aid of annotated training data. We employ agglomerative clustering to group word forms together using a metric that combines an orthographic distance and a semantic distance from word embeddings. We experiment with two variations of an edit distance-based model for quantifying orthographic distance, but, due to time constraints, our system does not improve over the shared task's baseline system.
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This work describes the Edinburgh submission to the SIGMORPHON 2021 Shared Task 2 on unsupervised morphological paradigm clustering. Given raw text input, the task was to assign each token to a cluster with other tokens from the same paradigm. We use
Adaptor Grammar segmentations combined with frequency-based heuristics to predict paradigm clusters. Our system achieved the highest average F1 score across 9 test languages, placing first out of 15 submissions.
We describe the second SIGMORPHON shared task on unsupervised morphology: the goal of the SIGMORPHON 2021 Shared Task on Unsupervised Morphological Paradigm Clustering is to cluster word types from a raw text corpus into paradigms. To this end, we re
lease corpora for 5 development and 9 test languages, as well as gold partial paradigms for evaluation. We receive 14 submissions from 4 teams that follow different strategies, and the best performing system is based on adaptor grammars. Results vary significantly across languages. However, all systems are outperformed by a supervised lemmatizer, implying that there is still room for improvement.
This paper describes the submission of the CU-UBC team for the SIGMORPHON 2021 Shared Task 2: Unsupervised morphological paradigm clustering. Our system generates paradigms using morphological transformation rules which are discovered from raw data.
We experiment with two methods for discovering rules. Our first approach generates prefix and suffix transformations between similar strings. Secondly, we experiment with more general rules which can apply transformations inside the input strings in addition to prefix and suffix transformations. We find that the best overall performance is delivered by prefix and suffix rules but more general transformation rules perform better for languages with templatic morphology and very high morpheme-to-word ratios.
With advances in neural language models, the focus of linguistic steganography has shifted from edit-based approaches to generation-based ones. While the latter's payload capacity is impressive, generating genuine-looking texts remains challenging. I
n this paper, we revisit edit-based linguistic steganography, with the idea that a masked language model offers an off-the-shelf solution. The proposed method eliminates painstaking rule construction and has a high payload capacity for an edit-based model. It is also shown to be more secure against automatic detection than a generation-based method while offering better control of the security/payload capacity trade-off.
Abstract We introduce an Edit-Based TransfOrmer with Repositioning (EDITOR), which makes sequence generation flexible by seamlessly allowing users to specify preferences in output lexical choice. Building on recent models for non-autoregressive seque
nce generation (Gu et al., 2019), EDITOR generates new sequences by iteratively editing hypotheses. It relies on a novel reposition operation designed to disentangle lexical choice from word positioning decisions, while enabling efficient oracles for imitation learning and parallel edits at decoding time. Empirically, EDITOR uses soft lexical constraints more effectively than the Levenshtein Transformer (Gu et al., 2019) while speeding up decoding dramatically compared to constrained beam search (Post and Vilar, 2018). EDITOR also achieves comparable or better translation quality with faster decoding speed than the Levenshtein Transformer on standard Romanian-English, English-German, and English-Japanese machine translation tasks.
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