Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userSemi-Supervised and Unsupervised Sense Annotation via Translations
الشرح شبه الإشراف والإشراف غير المنسق عبر الترجمات
208
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
لا يزال الاستحواذ على بيانات التدريب المتعدد اللغات يمثل تحديا في غزالة معنى الكلمة (WSD).لمعالجة هذه المشكلة، اقترحت النهج غير الخاضعة للكالة لإنشاء التعليقات التوضيحية بالمعنى تلقائيا لتدريب أنظمة WSD الخاضعة للإشراف.نقدم ثلاث طرق جديدة لإنشاء كوربورا المعشوفة بالشعور التي تستفيد الترجمات، وثبات الموازية، والموارد المعجمية، وكذلك تضمينات السياق والتركيب.تطبق أسلوبنا شبه الإشراف ترجمة الجهاز لنقل التعليقات التوضيحية القائمة إلى لغات أخرى.طرقنا اثنين من الأساليب غير الخاضعة لعمليات إعادة صياغة الشرح بالمعنى الناتج عن نظام WSD القائم على المعرفة عبر الترجمات المعجمية في كوربوس متوازي.نحصل على نتائج حديثة على معايير WSD القياسية.
Acquisition of multilingual training data continues to be a challenge in word sense disambiguation (WSD). To address this problem, unsupervised approaches have been proposed to automatically generate sense annotations for training supervised WSD systems. We present three new methods for creating sense-annotated corpora which leverage translations, parallel bitexts, lexical resources, as well as contextual and synset embeddings. Our semi-supervised method applies machine translation to transfer existing sense annotations to other languages. Our two unsupervised methods refine sense annotations produced by a knowledge-based WSD system via lexical translations in a parallel corpus. We obtain state-of-the-art results on standard WSD benchmarks.
References used
https://aclanthology.org/
rate research
Read More
To alleviate human efforts from obtaining large-scale annotations, Semi-Supervised Relation Extraction methods aim to leverage unlabeled data in addition to learning from limited samples. Existing self-training methods suffer from the gradual drift p
roblem, where noisy pseudo labels on unlabeled data are incorporated during training. To alleviate the noise in pseudo labels, we propose a method called MetaSRE, where a Relation Label Generation Network generates accurate quality assessment on pseudo labels by (meta) learning from the successful and failed attempts on Relation Classification Network as an additional meta-objective. To reduce the influence of noisy pseudo labels, MetaSRE adopts a pseudo label selection and exploitation scheme which assesses pseudo label quality on unlabeled samples and only exploits high-quality pseudo labels in a self-training fashion to incrementally augment labeled samples for both robustness and accuracy. Experimental results on two public datasets demonstrate the effectiveness of the proposed approach.
We describe the University of Alberta systems for the SemEval-2021 Word-in-Context (WiC) disambiguation task. We explore the use of translation information for deciding whether two different tokens of the same word correspond to the same sense of the
word. Our focus is on developing principled theoretical approaches which are grounded in linguistic phenomena, leading to more explainable models. We show that translations from multiple languages can be leveraged to improve the accuracy on the WiC task.
This paper presents a production Semi-Supervised Learning (SSL) pipeline based on the student-teacher framework, which leverages millions of unlabeled examples to improve Natural Language Understanding (NLU) tasks. We investigate two questions relate
d to the use of unlabeled data in production SSL context: 1) how to select samples from a huge unlabeled data pool that are beneficial for SSL training, and 2) how does the selected data affect the performance of different state-of-the-art SSL techniques. We compare four widely used SSL techniques, Pseudo-label (PL), Knowledge Distillation (KD), Virtual Adversarial Training (VAT) and Cross-View Training (CVT) in conjunction with two data selection methods including committee-based selection and submodular optimization based selection. We further examine the benefits and drawbacks of these techniques when applied to intent classification (IC) and named entity recognition (NER) tasks, and provide guidelines specifying when each of these methods might be beneficial to improve large scale NLU systems.
In this study, we proposed a novel Lexicon-based pseudo-labeling method utilizing explainable AI(XAI) approach. Existing approach have a fundamental limitation in their robustness because poor classifier leads to inaccurate soft-labeling, and it lead
to poor classifier repetitively. Meanwhile, we generate the lexicon consists of sentiment word based on the explainability score. Then we calculate the confidence of unlabeled data with lexicon and add them into labeled dataset for the robust pseudo-labeling approach. Our proposed method has three contributions. First, the proposed methodology automatically generates a lexicon based on XAI and performs independent pseudo-labeling, thereby guaranteeing higher performance and robustness compared to the existing one. Second, since lexicon-based pseudo-labeling is performed without re-learning in most of models, time efficiency is considerably increased, and third, the generated high-quality lexicon can be available for sentiment analysis of data from similar domains. The effectiveness and efficiency of our proposed method were verified through quantitative comparison with the existing pseudo-labeling method and qualitative review of the generated lexicon.
Weakly-supervised text classification aims to induce text classifiers from only a few user-provided seed words. The vast majority of previous work assumes high-quality seed words are given. However, the expert-annotated seed words are sometimes non-t
rivial to come up with. Furthermore, in the weakly-supervised learning setting, we do not have any labeled document to measure the seed words' efficacy, making the seed word selection process a walk in the dark''. In this work, we remove the need for expert-curated seed words by first mining (noisy) candidate seed words associated with the category names. We then train interim models with individual candidate seed words. Lastly, we estimate the interim models' error rate in an unsupervised manner. The seed words that yield the lowest estimated error rates are added to the final seed word set. A comprehensive evaluation of six binary classification tasks on four popular datasets demonstrates that the proposed method outperforms a baseline using only category name seed words and obtained comparable performance as a counterpart using expert-annotated seed words.
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
