Applying BERT Embeddings to Predict Legal Textual Entailment

Wehnert, Sabine; Dureja, Shipra; Kutty, Libin; Sudhi, Viju; Luca, Ernesto William De

doi:10.1007/s12626-022-00101-3

Cited by 6 publications

(3 citation statements)

References 16 publications

(23 reference statements)

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“…e corpus used in the intelligent translation model plays an important role [22]. e corpus can be used to store bilingual phrase data, accurately label the parts of speech of short words, standardize the function of each phrase, and improve the timeliness and accuracy of the automatic phrase recognition algorithm in the English machine translation process.…”

Section: Application Of Machine Learning Methods In Cross-mentioning

confidence: 99%

Cross-Context Accurate English Translation Method Based on the Machine Learning Model

Zhang¹

2022

Mathematical Problems in Engineering

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The era of big data and cloud computing has come, communication between different languages is becoming more and more common, and the barriers between languages are becoming more and more prominent. As the most important means to overcome language barriers, machine translation will play an increasingly important role in modern society. The previous machine translation technology has more or less disadvantages. The accuracy of translation is too low, which is a huge bottleneck hindering the further development of machine translation technology. Therefore, based on this, we can consider modeling the cross-context accurate English translation model based on the machine translation model and rely on the working principle of machine learning. This experiment shows that the translation accuracy of our method reaches 94.2%, which is higher than 39.5% of the benchmark method. This shows that the method in this paper can reduce the influence of other factors, ensure the accuracy of cross-context English translation to a certain extent, and meet the performance improvement requirements of the English translation system.

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Section: Application Of Machine Learning Methods In Cross-mentioning

confidence: 99%

Cross-Context Accurate English Translation Method Based on the Machine Learning Model

Zhang¹

2022

Mathematical Problems in Engineering

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“…Wehnert et al [27] have introduced three distinct methods for the classification of entailment. The first approach harmonizes Sentence-BERT embeddings with a graph neural network, while the second strategy leans on the specific LEGAL-BERT model, which undergoes additional training on the competition's retrieval task and is fine-tuned specifically for entailment classification.…”

Section: Background and Related Workmentioning

confidence: 99%

Deep Learning-based Sentence Embeddings using BERT for Textual Entailment

Alsuhaibani

2023

IJACSA

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This study directly and thoroughly investigates the practicalities of utilizing sentence embeddings, derived from the foundations of deep learning, for textual entailment recognition, with a specific emphasis on the robust BERT model. As a cornerstone of our research, we incorporated the Stanford Natural Language Inference (SNLI) dataset. Our study emphasizes a meticulous analysis of BERT's variable layers to ascertain the optimal layer for generating sentence embeddings that can effectively identify entailment. Our approach deviates from traditional methodologies, as we base our evaluation of entailment on the direct and simple comparison of sentence norms, subsequently highlighting the geometrical attributes of the embeddings. Experimental results revealed that the L2 norm of sentence embeddings, drawn specifically from BERT's 7th layer, emerged superior in entailment detection compared to other setups.

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“…The authors of BERT (Devlin et al, 2018) proposed an approach for determining the most optimal parameters for fine-tuning that is based on a search within a limited range. In this concept, the learn- (Wehnert et al, 2022;Rogers et al, 2020). Since those parameters don't always produce the best results and their use may still result in a model remaining undertrained, an alternative strategy was adopted for choosing the upper limit of training epochs that tracks validation loss and terminates training only when the conditions are met.…”

Section: Fine-tuningmentioning

confidence: 99%

AraLegal-BERT: A pretrained language model for Arabic Legal text

Al‐Qurishi¹,

Alqaseemi²,

Soussi³

2022

Preprint

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The effectiveness of the BERT model on multiple linguistic tasks has been well documented. On the other hand, its potentials for narrow and specific domains such as Legal, have not been fully explored. In this paper, we examine how BERT can be used in the Arabic legal domain and try customizing this language model for several downstream tasks using several different domain-relevant training and testing datasets to train BERT from scratch. We introduce the AraLegal-BERT, a bidirectional encoder Transformer-based model that have been thoroughly tested and carefully optimized with the goal to amplify the impact of NLP-driven solution concerning jurisprudence, legal documents, and legal practice. We fine-tuned AraLegal-BERT and evaluated it against three BERT variations for Arabic language in three natural languages understanding (NLU) tasks. The results show that the base version of AraLegal-BERT achieve better accuracy than the general and original BERT over the Legal text.

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Applying BERT Embeddings to Predict Legal Textual Entailment

Cited by 6 publications

References 16 publications

Cross-Context Accurate English Translation Method Based on the Machine Learning Model

Cross-Context Accurate English Translation Method Based on the Machine Learning Model

Deep Learning-based Sentence Embeddings using BERT for Textual Entailment

AraLegal-BERT: A pretrained language model for Arabic Legal text

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