Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userFindings of the SIGMORPHON 2021 Shared Task on Unsupervised Morphological Paradigm Clustering
نتائج SIGMORPHON 2021 المشتركة مهمة على تجميع النموذج المورفولوجي غير المقترح
329
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
نحن تصف مهمة Sigmorphon الثانية على التورفولوجيا غير المدعومة: الهدف من المهمة المشتركة SIGMORPHON 2021 على تجميع النماذج المورفولوجية غير المزدئة غير المنشأة هو أنواع الكلمات العنقودية من كوربوس نص الخام إلى النماذج.تحقيقا لهذه الغاية، نطلق سرورا لمدة 5 لغات تطوير و 9 لغات اختبار، وكذلك النماذج الجزئية الذهبية للتقييم.نتلقى 14 تقريرا من 4 فرق تتبع الاستراتيجيات المختلفة، ويستند أفضل نظام أداء على قواعد النحوية.تختلف النتائج بشكل كبير عبر اللغات.ومع ذلك، فإن جميع الأنظمة متفوقة من قبل Lemmmmmmmatizer تحت إشراف، مما يعني أنه لا يزال هناك مجال للتحسين.
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 release 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.
References used
https://aclanthology.org/
rate research
Read More
We report the results of the WMT 2021 shared task on Quality Estimation, where the challenge is to predict the quality of the output of neural machine translation systems at the word and sentence levels. This edition focused on two main novel additio
ns: (i) prediction for unseen languages, i.e. zero-shot settings, and (ii) prediction of sentences with catastrophic errors. In addition, new data was released for a number of languages, especially post-edited data. Participating teams from 19 institutions submitted altogether 1263 systems to different task variants and language pairs.
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.
The machine translation efficiency task challenges participants to make their systems faster and smaller with minimal impact on translation quality. How much quality to sacrifice for efficiency depends upon the application, so participants were encou
raged to make multiple submissions covering the space of trade-offs. In total, there were 53 submissions by 4 teams. There were GPU, single-core CPU, and multi-core CPU hardware tracks as well as batched throughput or single-sentence latency conditions. Submissions showed hundreds of millions of words can be translated for a dollar, average latency is 5--17 ms, and models fit in 7.5--150 MB.
We present the BME submission for the SIGMORPHON 2021 Task 0 Part 1, Generalization Across Typologically Diverse Languages shared task. We use an LSTM encoder-decoder model with three step training that is first trained on all languages, then fine-tu
ned on each language family and finally fine-tuned on individual languages. We use a different type of data augmentation technique in the first two steps. Our system outperformed the only other submission. Although it remains worse than the Transformer baseline released by the organizers, our model is simpler and our data augmentation techniques are easily applicable to new languages. We perform ablation studies and show that the augmentation techniques and the three training steps often help but sometimes have a negative effect. Our code is publicly available.
Morphological rules with various levels of specificity can be learned from example lexemes by recursive application of minimal generalization (Albright and Hayes, 2002, 2003).A model that learns rules solely through minimal generalization was used to
predict average human wug-test ratings from German, English, and Dutch in the SIGMORPHON-UniMorph 2021 Shared Task, with competitive results. Some formal properties of the minimal generalization operation were proved. An automatic method was developed to create wug-test stimuli for future experiments that investigate whether the model's morphological generalizations are too minimal.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
