Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userActiveEA: Active Learning for Neural Entity Alignment
أتصري: التعلم النشط لمحاذاة الكيان العصبي
334
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
تهدف محاذاة الكيان (EA) إلى مطابقة الكيانات المكافئة عبر الرسوم البيانية المعرفة المختلفة (KGS) وهي خطوة أساسية من KG Fusion. الأساليب الرئيسية الحالية - نماذج عصام العصبية - تعتمد على التدريب مع محاذاة البذور، أي مجموعة من أزواج كيان ما قبل الانحياز والتي تعد مكلفة للغاية للتعليق. في هذه الورقة، نركض إطارا للتعليم النشط الجديد (AL) من أجل EA العصبي، تهدف إلى إنشاء محاذاة بذرة مفيدة للغاية للحصول على نماذج EA أكثر فعالية مع تكلفة أقل تعلقا. يعالج إطارنا تحديين رئيسيتين واجههما عند تطبيق Alo EA: (1) كيفية استغلال التبعيات بين الكيانات داخل الإستراتيجية. تفترض معظم الاستراتيجيات أن مثيلات البيانات للعينة مستقلة وتوزيعها بشكل متطابقة. ومع ذلك، ترتبط الكيانات في كجم. لمعالجة هذا التحدي، نقترح استراتيجية أخذ العينات غير اليقين في الهيكل التي يمكن أن تقيس حالة عدم اليقين في كل كيان وتأثيرها على كياناته الجار في كجم. (2) كيفية التعرف على الكيانات التي تظهر في كجم واحد ولكن ليس في كجم آخر (I.E.، البكالوريوس). تحديد البكالوريوس من المرجح أن ينقذ ميزانية التعليق التوضيحي. لمعالجة هذا التحدي، نحن نضع المعرفة البكالوريوس يدفع الانتباه إلى تخفيف تأثير تحيز أخذ العينات. تظهر النتائج التجريبية أن استراتيجيتنا المقترحة يمكن أن تحسن بشكل كبير جودة أخذ العينات بعنادة جيدة عبر مجموعات البيانات المختلفة ونماذج EA ومبلغ البكالوريوس.
Entity Alignment (EA) aims to match equivalent entities across different Knowledge Graphs (KGs) and is an essential step of KG fusion. Current mainstream methods -- neural EA models -- rely on training with seed alignment, i.e., a set of pre-aligned entity pairs which are very costly to annotate. In this paper, we devise a novel Active Learning (AL) framework for neural EA, aiming to create highly informative seed alignment to obtain more effective EA models with less annotation cost. Our framework tackles two main challenges encountered when applying AL to EA: (1) How to exploit dependencies between entities within the AL strategy. Most AL strategies assume that the data instances to sample are independent and identically distributed. However, entities in KGs are related. To address this challenge, we propose a structure-aware uncertainty sampling strategy that can measure the uncertainty of each entity as well as its impact on its neighbour entities in the KG. (2) How to recognise entities that appear in one KG but not in the other KG (i.e., bachelors). Identifying bachelors would likely save annotation budget. To address this challenge, we devise a bachelor recognizer paying attention to alleviate the effect of sampling bias. Empirical results show that our proposed AL strategy can significantly improve sampling quality with good generality across different datasets, EA models and amount of bachelors.
References used
https://aclanthology.org/
rate research
Read More
Neural machine translation (NMT) is sensitive to domain shift. In this paper, we address this problem in an active learning setting where we can spend a given budget on translating in-domain data, and gradually fine-tune a pre-trained out-of-domain N
MT model on the newly translated data. Existing active learning methods for NMT usually select sentences based on uncertainty scores, but these methods require costly translation of full sentences even when only one or two key phrases within the sentence are informative. To address this limitation, we re-examine previous work from the phrase-based machine translation (PBMT) era that selected not full sentences, but rather individual phrases. However, while incorporating these phrases into PBMT systems was relatively simple, it is less trivial for NMT systems, which need to be trained on full sequences to capture larger structural properties of sentences unique to the new domain. To overcome these hurdles, we propose to select both full sentences and individual phrases from unlabelled data in the new domain for routing to human translators. In a German-English translation task, our active learning approach achieves consistent improvements over uncertainty-based sentence selection methods, improving up to 1.2 BLEU score over strong active learning baselines.
High-quality arguments are an essential part of decision-making. Automatically predicting the quality of an argument is a complex task that recently got much attention in argument mining. However, the annotation effort for this task is exceptionally
high. Therefore, we test uncertainty-based active learning (AL) methods on two popular argument-strength data sets to estimate whether sample-efficient learning can be enabled. Our extensive empirical evaluation shows that uncertainty-based acquisition functions can not surpass the accuracy reached with the random acquisition on these data sets.
Interactive-predictive translation is a collaborative iterative process and where human translators produce translations with the help of machine translation (MT) systems interactively. Various sampling techniques in active learning (AL) exist to upd
ate the neural MT (NMT) model in the interactive-predictive scenario. In this paper and we explore term based (named entity count (NEC)) and quality based (quality estimation (QE) and sentence similarity (Sim)) sampling techniques -- which are used to find the ideal candidates from the incoming data -- for human supervision and MT model's weight updation. We carried out experiments with three language pairs and viz. German-English and Spanish-English and Hindi-English. Our proposed sampling technique yields 1.82 and 0.77 and 0.81 BLEU points improvements for German-English and Spanish-English and Hindi-English and respectively and over random sampling based baseline. It also improves the present state-of-the-art by 0.35 and 0.12 BLEU points for German-English and Spanish-English and respectively. Human editing effort in terms of number-of-words-changed also improves by 5 and 4 points for German-English and Spanish-English and respectively and compared to the state-of-the-art.
Document alignment techniques based on multilingual sentence representations have recently shown state of the art results. However, these techniques rely on unsupervised distance measurement techniques, which cannot be fined-tuned to the task at hand
. In this paper, instead of these unsupervised distance measurement techniques, we employ Metric Learning to derive task-specific distance measurements. These measurements are supervised, meaning that the distance measurement metric is trained using a parallel dataset. Using a dataset belonging to English, Sinhala, and Tamil, which belong to three different language families, we show that these task-specific supervised distance learning metrics outperform their unsupervised counterparts, for document alignment.
While the predictive performance of modern statistical dependency parsers relies heavily on the availability of expensive expert-annotated treebank data, not all annotations contribute equally to the training of the parsers. In this paper, we attempt
to reduce the number of labeled examples needed to train a strong dependency parser using batch active learning (AL). In particular, we investigate whether enforcing diversity in the sampled batches, using determinantal point processes (DPPs), can improve over their diversity-agnostic counterparts. Simulation experiments on an English newswire corpus show that selecting diverse batches with DPPs is superior to strong selection strategies that do not enforce batch diversity, especially during the initial stages of the learning process. Additionally, our diversity-aware strategy is robust under a corpus duplication setting, where diversity-agnostic sampling strategies exhibit significant degradation.
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
