IIE-NLP-NUT at SemEval-2020 Task 4: Guiding PLM with Prompt Template Reconstruction Strategy for ComVE

• URL: http://arxiv.org/abs/2007.00924v1
• Date: Thu, 2 Jul 2020 06:59:53 GMT
• Title: IIE-NLP-NUT at SemEval-2020 Task 4: Guiding PLM with Prompt Template Reconstruction Strategy for ComVE
• Authors: Luxi Xing, Yuqiang Xie, Yue Hu, Wei Peng
• Abstract summary: We formalize the subtasks into the multiple-choice question answering format and construct the input with the prompt templates. Experimental results show that our approaches achieve significant performance compared with the baseline systems. Our approaches secure the third rank on both official test sets of the first two subtasks with an accuracy of 96.4 and an accuracy of 94.3 respectively.
• Score: 13.334749848189826
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces our systems for the first two subtasks of SemEval Task4: Commonsense Validation and Explanation. To clarify the intention for judgment and inject contrastive information for selection, we propose the input reconstruction strategy with prompt templates. Specifically, we formalize the subtasks into the multiple-choice question answering format and construct the input with the prompt templates, then, the final prediction of question answering is considered as the result of subtasks. Experimental results show that our approaches achieve significant performance compared with the baseline systems. Our approaches secure the third rank on both official test sets of the first two subtasks with an accuracy of 96.4 and an accuracy of 94.3 respectively.

Related papers

• Prompt Algebra for Task Composition [131.97623832435812]
  We consider Visual Language Models with prompt tuning as our base classifier. We propose constrained prompt tuning to improve performance of the composite classifier. On UTZappos it improves classification accuracy over the best base model by 8.45% on average.
  arXiv  Detail & Related papers  (2023-06-01T03:20:54Z)
• Instruction Tuning for Few-Shot Aspect-Based Sentiment Analysis [72.9124467710526]
  generative approaches have been proposed to extract all four elements as (one or more) quadruplets from text as a single task. We propose a unified framework for solving ABSA, and the associated sub-tasks to improve the performance in few-shot scenarios.
  arXiv  Detail & Related papers  (2022-10-12T23:38:57Z)
• PANDA: Prompt Transfer Meets Knowledge Distillation for Efficient Model Adaptation [89.0074567748505]
  We propose a new metric to accurately predict the prompt transferability (regarding (i)), and a novel PoT approach (namely PANDA) Our proposed metric works well to predict the prompt transferability; 2) our PANDA consistently outperforms the vanilla PoT approach by 2.3% average score (up to 24.1%) among all tasks and model sizes; 3) with our PANDA approach, prompt-tuning can achieve competitive and even better performance than model-tuning in various PLM scales scenarios.
  arXiv  Detail & Related papers  (2022-08-22T09:14:14Z)
• AdaPrompt: Adaptive Model Training for Prompt-based NLP [77.12071707955889]
  We propose AdaPrompt, adaptively retrieving external data for continual pretraining of PLMs. Experimental results on five NLP benchmarks show that AdaPrompt can improve over standard PLMs in few-shot settings. In zero-shot settings, our method outperforms standard prompt-based methods by up to 26.35% relative error reduction.
  arXiv  Detail & Related papers  (2022-02-10T04:04:57Z)
• In Situ Answer Sentence Selection at Web-scale [120.69820139008138]
  Passage-based Extracting Answer Sentence In-place (PEASI) is a novel design for AS2 optimized for Web-scale setting. We train PEASI in a multi-task learning framework that encourages feature sharing between the components: passage reranker and passage-based answer sentence extractor. Experiments show PEASI effectively outperforms the current state-of-the-art setting for AS2, i.e., a point-wise model for ranking sentences independently, by 6.51% in accuracy.
  arXiv  Detail & Related papers  (2022-01-16T06:36:00Z)
• ZJUKLAB at SemEval-2021 Task 4: Negative Augmentation with Language Model for Reading Comprehension of Abstract Meaning [16.151203366447962]
  We explain the algorithms used to learn our models and the process of tuning the algorithms and selecting the best model. Inspired by the similarity of the ReCAM task and the language pre-training, we propose a simple yet effective technology, namely, negative augmentation with language model. Our models achieve the 4th rank on both official test sets of Subtask 1 and Subtask 2 with an accuracy of 87.9% and an accuracy of 92.8%, respectively.
  arXiv  Detail & Related papers  (2021-02-25T13:03:05Z)
• IIE-NLP-Eyas at SemEval-2021 Task 4: Enhancing PLM for ReCAM with Special Tokens, Re-Ranking, Siamese Encoders and Back Translation [8.971288666318719]
  This paper introduces our systems for all three subtasks of SemEval-2021 Task 4: Reading of Abstract Meaning. We well-design many simple and effective approaches adapted to the backbone model (RoBERTa) Experimental results show that our approaches achieve significant performance compared with the baseline systems.
  arXiv  Detail & Related papers  (2021-02-25T10:51:48Z)
• LRG at SemEval-2021 Task 4: Improving Reading Comprehension with Abstract Words using Augmentation, Linguistic Features and Voting [0.6850683267295249]
  Given a fill-in-the-blank-type question, the task is to predict the most suitable word from a list of 5 options. We use encoders of transformers-based models pre-trained on the masked language modelling (MLM) task to build our Fill-in-the-blank (FitB) models. We propose variants, namely Chunk Voting and Max Context, to take care of input length restrictions for BERT, etc.
  arXiv  Detail & Related papers  (2021-02-24T12:33:12Z)
• LMVE at SemEval-2020 Task 4: Commonsense Validation and Explanation using Pretraining Language Model [5.428461405329692]
  This paper describes our submission to subtask a and b of SemEval-2020 Task 4. For subtask a, we use a ALBERT based model with improved input form to pick out the common sense statement from two statement candidates. For subtask b, we use a multiple choice model enhanced by hint sentence mechanism to select the reason from given options about why a statement is against common sense.
  arXiv  Detail & Related papers  (2020-07-06T05:51:10Z)
• Yseop at SemEval-2020 Task 5: Cascaded BERT Language Model for Counterfactual Statement Analysis [0.0]
  We use a BERT base model for the classification task and build a hybrid BERT Multi-Layer Perceptron system to handle the sequence identification task. Our experiments show that while introducing syntactic and semantic features does little in improving the system in the classification task, using these types of features as cascaded linear inputs to fine-tune the sequence-delimiting ability of the model ensures it outperforms other similar-purpose complex systems like BiLSTM-CRF in the second task.
  arXiv  Detail & Related papers  (2020-05-18T08:19:18Z)
• A Simple Language Model for Task-Oriented Dialogue [61.84084939472287]
  SimpleTOD is a simple approach to task-oriented dialogue that uses a single, causal language model trained on all sub-tasks recast as a single sequence prediction problem. This allows SimpleTOD to fully leverage transfer learning from pre-trained, open domain, causal language models such as GPT-2.
  arXiv  Detail & Related papers  (2020-05-02T11:09:27Z)

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.