AutoRC: Improving BERT Based Relation Classification Models via Architecture Search

Zhu, Wei

doi:10.18653/v1/2021.acl-srw.4

Cited by 8 publications

(4 citation statements)

References 18 publications

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“…Luo et al [26] proposed a BERT-based approach for single-relation question answering (SR-QA), which consists of two models, entity linking and relation detection. Zhu et al [27] designed a comprehensive search space for BERT based relation classification models and employ neural architecture search method to automatically discover the design choices. However, in different situations, the best-performance model is also different.…”

Section: Related Workmentioning

confidence: 99%

A More Robust Model to Answer Noisy Questions in KBQA

Wang

Xü

et al. 2023

IEEE Access

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In practical applications, the raw input to a Knowledge Based Question Answering (KBQA) system may vary in forms, expressions, sources, etc. As a result, the actual input to the system may contain various errors caused by various noise in raw data and processes of transmission, transformation, translation, etc. As a result, it is significant to evaluate and enhance the robustness of a KBQA model to various noisy questions. In this paper, we generate 29 datasets of various noisy questions based on the original SimpleQuestions dataset to evaluate and enhance the robustness of a KBQA model, and propose a model which is more robust to various noisy questions. Compared with traditional methods, the main contribution in this paper is that we propose a method of generating datasets of different noisy questions to evaluate the robustness of a KBQA model, and propose a KBQA model which contains incremental learning and Mask Language Model (MLM) in the question answering process, so that our model is less affected by different kinds of noise in questions and achieves higher accuracies on datasets of different noisy questions, which shows its robustness. Experimental results show that our model achieves an average accuracy of 78.1% on these datasets and outperforms the baseline BERT-based model by an average margin of 5.0% with the similar training cost. In addition, further experiments show that our model is compatible with other pre-trained models such as ALBERT and ELECTRA.

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Section: Related Workmentioning

confidence: 99%

A More Robust Model to Answer Noisy Questions in KBQA

Wang

Xü

et al. 2023

IEEE Access

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“…Luo et al [19] proposed a BERT-based approach for single-relation question answering (SR-QA), which consists of two models, entity linking, and relation detection. Zhu [20] designed a comprehensive search space for BERT-based relation classifcation models and employ neural architecture search method to automatically discover the design choices. However, in diferent situations, the best-performance model is also diferent.…”

Section: Related Workmentioning

confidence: 99%

“…if relationinR 2 and subjectnot in S 1 then S 1 .append(subject) (15) end for (16) for s in S 1 do (17) Calculate Score III (q, s) by Pruning Model III (18) ifScore III (q, s) > α then S 2 .append(s) (19) end for (20) for s in S 2 do (21) Calculate Score IV (q, s) by Pruning Model IV (22) X.append(Score IV ) (23) end for (24) s � argmax(X) (25) return s ALGORITHM 1: Te core algorithm of MGPM. performance on some specifc type datasets as the comparison of our model and experimental results show that our model achieves a better performance, which shows the efect of model.…”

Section: Experiments Settingmentioning

confidence: 99%

Multigranularity Pruning Model for Subject Recognition Task under Knowledge Base Question Answering When General Models Fail

Wang,

Xu,

Song

et al. 2023

International Journal of Intelligent Systems

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In general knowledge base question answering (KBQA) models, subject recognition (SR) is usually a precondition of finding an answer, and it is a common way to employ a general named entity recognition (NER) model such as BERT-CRF to recognize the subject. However, in previous researches, the difference between a NER task and a SR task is usually ignored, and a wrong entity recognized by the NER model will certainly lead to a wrong answer in the KBQA task, which is one bottleneck for KBQA performance. In this paper, a multigranularity pruning model (MGPM) is proposed to answer a question when general models fail to recognize a subject. In MGPM, the set of all possible subjects in the Knowledge Base (KB) is pruned by 4 multigranularity pruning submodels successively based on the constraint of relation (domain and tuple), string similarity, and semantic similarity. Experimental results show that our model is compatible with various KBQA models for both single-relation and complex questions answering. The integrated MGPM model (with the BERT-CRF model) achieves a SR accuracy of 94.4% on the SimpleQuestions dataset, 68.6% on the WebQuestionsSP dataset, and 63.7% on the WebQuestions dataset, which outperforms the original model by a margin of 3.6%, 8.6%, and 5.3%, respectively.

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“…Luo et al [19] proposed a BERT-based approach for single-relation question answering (SR-QA), which consists of two models, entity linking, and relation detection. Zhu et al [20] designed a comprehensive search space for BERT-based relation classification models and employed the neural architecture search method to automatically discover the design choices. However, in different situations, the best-performance model is also different.…”

Section: Related Workmentioning

confidence: 99%

An Improved Nested Named-Entity Recognition Model for Subject Recognition Task under Knowledge Base Question Answering

Wang,

Xu,

et al. 2023

Applied Sciences

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In the subject recognition (SR) task under Knowledge Base Question Answering (KBQA), a common method is by training and employing a general flat Named-Entity Recognition (NER) model. However, it is not effective and robust enough in the case that the recognized entity could not be strictly matched to any subjects in the Knowledge Base (KB). Compared to flat NER models, nested NER models show more flexibility and robustness in general NER tasks, whereas it is difficult to employ a nested NER model directly in an SR task. In this paper, we take advantage of features of a nested NER model and propose an Improved Nested NER Model (INNM) for the SR task under KBQA. In our model, each question token is labeled as either an entity token, a start token, or an end token by a modified nested NER model based on semantics. Then, entity candidates would be generated based on such labels, and an approximate matching strategy is employed to score all subjects in the KB based on string similarity to find the best-matched subject. Experimental results show that our model is effective and robust to both single-relation questions and complex questions, which outperforms the baseline flat NER model by a margin of 3.3% accuracy on the SimpleQuestions dataset and a margin of 11.0% accuracy on the WebQuestionsSP dataset.

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AutoRC: Improving BERT Based Relation Classification Models via Architecture Search

Cited by 8 publications

References 18 publications

A More Robust Model to Answer Noisy Questions in KBQA

A More Robust Model to Answer Noisy Questions in KBQA

Multigranularity Pruning Model for Subject Recognition Task under Knowledge Base Question Answering When General Models Fail

An Improved Nested Named-Entity Recognition Model for Subject Recognition Task under Knowledge Base Question Answering

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