Autoencoding Language Model Based Ensemble Learning for Commonsense Validation and Explanation

• URL: http://arxiv.org/abs/2204.03324v1
• Date: Thu, 7 Apr 2022 09:43:51 GMT
• Title: Autoencoding Language Model Based Ensemble Learning for Commonsense Validation and Explanation
• Authors: Ngo Quang Huy, Tu Minh Phuong and Ngo Xuan Bach
• Abstract summary: We present an Autoencoding Language Model based Ensemble learning method for commonsense validation and explanation. Our method can distinguish natural language statements that are against commonsense (validation subtask) and correctly identify the reason for making against commonsense (explanation selection subtask) Experimental results on the benchmark dataset of SemEval-2020 Task 4 show that our method outperforms state-of-the-art models.
• Score: 1.503974529275767
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: An ultimate goal of artificial intelligence is to build computer systems that can understand human languages. Understanding commonsense knowledge about the world expressed in text is one of the foundational and challenging problems to create such intelligent systems. As a step towards this goal, we present in this paper ALMEn, an Autoencoding Language Model based Ensemble learning method for commonsense validation and explanation. By ensembling several advanced pre-trained language models including RoBERTa, DeBERTa, and ELECTRA with Siamese neural networks, our method can distinguish natural language statements that are against commonsense (validation subtask) and correctly identify the reason for making against commonsense (explanation selection subtask). Experimental results on the benchmark dataset of SemEval-2020 Task 4 show that our method outperforms state-of-the-art models, reaching 97.9% and 95.4% accuracies on the validation and explanation selection subtasks, respectively.

Related papers

• Learning Phonotactics from Linguistic Informants [54.086544221761486]
  Our model iteratively selects or synthesizes a data-point according to one of a range of information-theoretic policies. We find that the information-theoretic policies that our model uses to select items to query the informant achieve sample efficiency comparable to, or greater than, fully supervised approaches.
  arXiv  Detail & Related papers  (2024-05-08T00:18:56Z)
• A Computational Model for the Assessment of Mutual Intelligibility Among Closely Related Languages [1.5773159234875098]
  Closely related languages show linguistic similarities that allow speakers of one language to understand speakers of another language without having actively learned it. Mutual intelligibility varies in degree and is typically tested in psycholinguistic experiments. We propose a computer-assisted method using the Linear Discriminative Learner to approximate the cognitive processes by which humans learn languages.
  arXiv  Detail & Related papers  (2024-02-05T11:32:13Z)
• Self Generated Wargame AI: Double Layer Agent Task Planning Based on Large Language Model [0.6562256987706128]
  This paper innovatively applies the large language model to the field of intelligent decision-making. It proposes a two-layer agent task planning, issues and executes decision commands through the interaction of natural language. It is found that the intelligent decision-making ability of the large language model is significantly stronger than the commonly used reinforcement learning AI and rule AI.
  arXiv  Detail & Related papers  (2023-12-02T09:45:45Z)
• Commonsense Knowledge Transfer for Pre-trained Language Models [83.01121484432801]
  We introduce commonsense knowledge transfer, a framework to transfer the commonsense knowledge stored in a neural commonsense knowledge model to a general-purpose pre-trained language model. It first exploits general texts to form queries for extracting commonsense knowledge from the neural commonsense knowledge model. It then refines the language model with two self-supervised objectives: commonsense mask infilling and commonsense relation prediction.
  arXiv  Detail & Related papers  (2023-06-04T15:44:51Z)
• Lila: A Unified Benchmark for Mathematical Reasoning [59.97570380432861]
  LILA is a unified mathematical reasoning benchmark consisting of 23 diverse tasks along four dimensions. We construct our benchmark by extending 20 datasets benchmark by collecting task instructions and solutions in the form of Python programs. We introduce BHASKARA, a general-purpose mathematical reasoning model trained on LILA.
  arXiv  Detail & Related papers  (2022-10-31T17:41:26Z)
• Context-Aware Language Modeling for Goal-Oriented Dialogue Systems [84.65707332816353]
  We formulate goal-oriented dialogue as a partially observed Markov decision process. We derive a simple and effective method to finetune language models in a goal-aware way. We evaluate our method on a practical flight-booking task using AirDialogue.
  arXiv  Detail & Related papers  (2022-04-18T17:23:11Z)
• Towards Zero-shot Language Modeling [90.80124496312274]
  We construct a neural model that is inductively biased towards learning human languages. We infer this distribution from a sample of typologically diverse training languages. We harness additional language-specific side information as distant supervision for held-out languages.
  arXiv  Detail & Related papers  (2021-08-06T23:49:18Z)
• Exploring Software Naturalness through Neural Language Models [56.1315223210742]
  The Software Naturalness hypothesis argues that programming languages can be understood through the same techniques used in natural language processing. We explore this hypothesis through the use of a pre-trained transformer-based language model to perform code analysis tasks.
  arXiv  Detail & Related papers  (2020-06-22T21:56:14Z)

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.