Boosting Transformers for Job Expression Extraction and Classification in a Low-Resource Setting

• URL: http://arxiv.org/abs/2109.08597v1
• Date: Fri, 17 Sep 2021 15:21:02 GMT
• Title: Boosting Transformers for Job Expression Extraction and Classification in a Low-Resource Setting
• Authors: Lukas Lange and Heike Adel and Jannik Str\"otgen
• Abstract summary: We present our approaches to tackle the extraction and classification of job expressions in Spanish texts. As neither language nor domain experts, we experiment with the multilingual XLM-R transformer model. Our results show strong improvements using these methods by up to 5.3 F1 points compared to a fine-tuned XLM-R model.
• Score: 12.489741131691737
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we explore possible improvements of transformer models in a low-resource setting. In particular, we present our approaches to tackle the first two of three subtasks of the MEDDOPROF competition, i.e., the extraction and classification of job expressions in Spanish clinical texts. As neither language nor domain experts, we experiment with the multilingual XLM-R transformer model and tackle these low-resource information extraction tasks as sequence-labeling problems. We explore domain- and language-adaptive pretraining, transfer learning and strategic datasplits to boost the transformer model. Our results show strong improvements using these methods by up to 5.3 F1 points compared to a fine-tuned XLM-R model. Our best models achieve 83.2 and 79.3 F1 for the first two tasks, respectively.

Related papers

• Few-shot learning for automated content analysis: Efficient coding of arguments and claims in the debate on arms deliveries to Ukraine [0.9576975587953563]
  Pre-trained language models (PLM) based on transformer neural networks offer great opportunities to improve automatic content analysis in communication science. Three characteristics so far impeded the widespread adoption of the methods in the applying disciplines: the dominance of English language models in NLP research, the necessary computing resources, and the effort required to produce training data to fine-tune PLMs. We test our approach on a realistic use case from communication science to automatically detect claims and arguments together with their stance in the German news debate on arms deliveries to Ukraine.
  arXiv  Detail & Related papers  (2023-12-28T11:39:08Z)
• Evaluating Prompt-based Question Answering for Object Prediction in the Open Research Knowledge Graph [0.0]
  This work reports results on adopting prompt-based training of transformers for textitscholarly knowledge graph object prediction It deviates from the other works proposing entity and relation extraction pipelines for predicting objects of a scholarly knowledge graph. We find that (i) per expectations, transformer models when tested out-of-the-box underperform on a new domain of data, (ii) prompt-based training of the models achieve performance boosts of up to 40% in a relaxed evaluation setting.
  arXiv  Detail & Related papers  (2023-05-22T10:35:18Z)
• Adapting Sentence Transformers for the Aviation Domain [0.8437187555622164]
  We propose a novel approach for adapting sentence transformers for the aviation domain. Our method is a two-stage process consisting of pre-training followed by fine-tuning. Our work highlights the importance of domain-specific adaptation in developing high-quality NLP solutions for specialized industries like aviation.
  arXiv  Detail & Related papers  (2023-05-16T15:53:24Z)
• Pre-trained Language Models for Keyphrase Generation: A Thorough Empirical Study [76.52997424694767]
  We present an in-depth empirical study of keyphrase extraction and keyphrase generation using pre-trained language models. We show that PLMs have competitive high-resource performance and state-of-the-art low-resource performance. Further results show that in-domain BERT-like PLMs can be used to build strong and data-efficient keyphrase generation models.
  arXiv  Detail & Related papers  (2022-12-20T13:20:21Z)
• Adapted Multimodal BERT with Layer-wise Fusion for Sentiment Analysis [84.12658971655253]
  We propose Adapted Multimodal BERT, a BERT-based architecture for multimodal tasks. adapter adjusts the pretrained language model for the task at hand, while the fusion layers perform task-specific, layer-wise fusion of audio-visual information with textual BERT representations. In our ablations we see that this approach leads to efficient models, that can outperform their fine-tuned counterparts and are robust to input noise.
  arXiv  Detail & Related papers  (2022-12-01T17:31:42Z)
• Exploring the State-of-the-Art Language Modeling Methods and Data Augmentation Techniques for Multilingual Clause-Level Morphology [3.8498574327875947]
  We present our work on all three parts of the shared task: inflection, reinflection, and analysis. We mainly explore two approaches: Transformer models in combination with data augmentation, and exploiting the state-of-the-art language modeling techniques for morphological analysis. Our methods achieved first place in each of the three tasks and outperforms mT5-baseline with 89% for inflection, 80% for reinflection and 12% for analysis.
  arXiv  Detail & Related papers  (2022-11-03T11:53:39Z)
• Leveraging Pre-trained Models for Failure Analysis Triplets Generation [0.0]
  We leverage the attention mechanism of pre-trained causal language models such as Transformer model for the downstream task of generating Failure Analysis Triplets (FATs) We observe that Generative Pre-trained Transformer 2 (GPT2) outperformed other transformer model for the failure analysis triplet generation (FATG) task. In particular, we observe that GPT2 (trained on 1.5B parameters) outperforms pre-trained BERT, BART and GPT3 by a large margin on ROUGE.
  arXiv  Detail & Related papers  (2022-10-31T17:21:15Z)
• Pre-training Transformer Models with Sentence-Level Objectives for Answer Sentence Selection [99.59693674455582]
  We propose three novel sentence-level transformer pre-training objectives that incorporate paragraph-level semantics within and across documents. Our experiments on three public and one industrial AS2 datasets demonstrate the empirical superiority of our pre-trained transformers over baseline models.
  arXiv  Detail & Related papers  (2022-05-20T22:39:00Z)
• Bayesian Transformer Language Models for Speech Recognition [59.235405107295655]
  State-of-the-art neural language models (LMs) represented by Transformers are highly complex. This paper proposes a full Bayesian learning framework for Transformer LM estimation.
  arXiv  Detail & Related papers  (2021-02-09T10:55:27Z)
• Rethinking Document-level Neural Machine Translation [73.42052953710605]
  We try to answer the question: Is the capacity of current models strong enough for document-level translation? We observe that the original Transformer with appropriate training techniques can achieve strong results for document translation, even with a length of 2000 words.
  arXiv  Detail & Related papers  (2020-10-18T11:18:29Z)
• Dynamic Data Selection and Weighting for Iterative Back-Translation [116.14378571769045]
  We propose a curriculum learning strategy for iterative back-translation models. We evaluate our models on domain adaptation, low-resource, and high-resource MT settings. Experimental results demonstrate that our methods achieve improvements of up to 1.8 BLEU points over competitive baselines.
  arXiv  Detail & Related papers  (2020-04-07T19:49:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.