Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userKnowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training
الرسم البياني المعرفة القائم على جيل Corpus الاصطناعي لنموذج اللغة المحسنة المعرفة قبل التدريب
380
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
يعمل العمل المسبق على جيل البيانات إلى النص، ومهمة تحويل الكلام الرسم البياني (KG) ثلاث مرات إلى نص طبيعي، يركز على مجموعات البيانات القياسية الخاصة بالمجال. ومع ذلك، في هذه الورقة، فإننا ننفذنا اللغة الإنجليزية بأكملها Wikidata KG، ومناقشة التحديات الفريدة المرتبطة بمجال واسع ومجموع واسع النطاق. نوضح كذلك بأنه لفظي كجم شامل ومكون من كجم مثل Wikidata يمكن استخدامه لدمج KGS الهيكلية واللغات الطبيعية. على عكس العديد من البنيات التي تم تطويرها لدمج هاتين المصدرين، فإن نهجنا يحول كجم إلى نص طبيعي، مما يسمح له بالدمج بسلاسة في نماذج اللغة الحالية. إنه يحمل مزايا أخرى لتحسين الدقة الواقعية وتقليل السمية في نموذج اللغة الناتج. نقوم بتقييم هذا النهج عن طريق زيادة عملية استرجاع النموذج لغوي استرجاع وإظهار تحسينات كبيرة على مهام المعرفة المكثفة في المجال المفتوح وكثير المعرفة LAMA.
Prior work on Data-To-Text Generation, the task of converting knowledge graph (KG) triples into natural text, focused on domain-specific benchmark datasets. In this paper, however, we verbalize the entire English Wikidata KG, and discuss the unique challenges associated with a broad, open-domain, large-scale verbalization. We further show that verbalizing a comprehensive, encyclopedic KG like Wikidata can be used to integrate structured KGs and natural language corpora. In contrast to the many architectures that have been developed to integrate these two sources, our approach converts the KG into natural text, allowing it to be seamlessly integrated into existing language models. It carries the further advantages of improved factual accuracy and reduced toxicity in the resulting language model. We evaluate this approach by augmenting the retrieval corpus in a retrieval language model and showing significant improvements on the knowledge intensive tasks of open domain QA and the LAMA knowledge probe.
References used
https://aclanthology.org/
rate research
Read More
Relations in most of the traditional knowledge graphs (KGs) only reflect static and factual connections, but fail to represent the dynamic activities and state changes about entities. In this paper, we emphasize the importance of incorporating events
in KG representation learning, and propose an event-enhanced KG embedding model EventKE. Specifically, given the original KG, we first incorporate event nodes by building a heterogeneous network, where entity nodes and event nodes are distributed on the two sides of the network inter-connected by event argument links. We then use entity-entity relations from the original KG and event-event temporal links to inner-connect entity and event nodes respectively. We design a novel and effective attention-based message passing method, which is conducted on entity-entity, event-entity, and event-event relations to fuse the event information into KG embeddings. Experimental results on real-world datasets demonstrate that events can greatly improve the quality of the KG embeddings on multiple downstream tasks.
Knowledge graph embedding, representing entities and relations in the knowledge graphs with high-dimensional vectors, has made significant progress in link prediction. More researchers have explored the representational capabilities of models in rece
nt years. That is, they investigate better representational models to fit symmetry/antisymmetry and combination relationships. The current embedding models are more inclined to utilize the identical vector for the same entity in various triples to measure the matching performance. The observation that measuring the rationality of specific triples means comparing the matching degree of the specific attributes associated with the relations is well-known. Inspired by this fact, this paper designs Semantic Filter Based on Relations(SFBR) to extract the required attributes of the entities. Then the rationality of triples is compared under these extracted attributes through the traditional embedding models. The semantic filter module can be added to most geometric and tensor decomposition models with minimal additional memory. experiments on the benchmark datasets show that the semantic filter based on relations can suppress the impact of other attribute dimensions and improve link prediction performance. The tensor decomposition models with SFBR have achieved state-of-the-art.
Dialogue State Tracking is central to multi-domain task-oriented dialogue systems, responsible for extracting information from user utterances. We present a novel hybrid architecture that augments GPT-2 with representations derived from Graph Attenti
on Networks in such a way to allow causal, sequential prediction of slot values. The model architecture captures inter-slot relationships and dependencies across domains that otherwise can be lost in sequential prediction. We report improvements in state tracking performance in MultiWOZ 2.0 against a strong GPT-2 baseline and investigate a simplified sparse training scenario in which DST models are trained only on session-level annotations but evaluated at the turn level. We further report detailed analyses to demonstrate the effectiveness of graph models in DST by showing that the proposed graph modules capture inter-slot dependencies and improve the predictions of values that are common to multiple domains.
An exciting frontier in natural language understanding (NLU) and generation (NLG) calls for (vision-and-) language models that can efficiently access external structured knowledge repositories. However, many existing knowledge bases only cover limite
d domains, or suffer from noisy data, and most of all are typically hard to integrate into neural language pipelines. To fill this gap, we release VisualSem: a high-quality knowledge graph (KG) which includes nodes with multilingual glosses, multiple illustrative images, and visually relevant relations. We also release a neural multi-modal retrieval model that can use images or sentences as inputs and retrieves entities in the KG. This multi-modal retrieval model can be integrated into any (neural network) model pipeline. We encourage the research community to use VisualSem for data augmentation and/or as a source of grounding, among other possible uses. VisualSem as well as the multi-modal retrieval models are publicly available and can be downloaded in this URL: https://github.com/iacercalixto/visualsem.
In this work, we present an information-theoretic framework that formulates cross-lingual language model pre-training as maximizing mutual information between multilingual-multi-granularity texts. The unified view helps us to better understand the ex
isting methods for learning cross-lingual representations. More importantly, inspired by the framework, we propose a new pre-training task based on contrastive learning. Specifically, we regard a bilingual sentence pair as two views of the same meaning and encourage their encoded representations to be more similar than the negative examples. By leveraging both monolingual and parallel corpora, we jointly train the pretext tasks to improve the cross-lingual transferability of pre-trained models. Experimental results on several benchmarks show that our approach achieves considerably better performance. The code and pre-trained models are available at https://aka.ms/infoxlm.
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
