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检索条件"任意字段=Conference on empirical methods in natural language processing"

共 15258 条记录，以下是4711-4720 订阅

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UNLPS at TextGraphs-16 natural language Premise Selection Task: Unsupervised natural language Premise Selection in Mathematical Text using Sentence-MPNet 16

UNLPS at TextGraphs-16 Natural Language Premise Selection Ta...

引用

16th Workshop on Graph-Based methods for natural language processing, TextGraphs 2022, in conjunction with the 29th International conference on Computational Linguistics, COLING 2022

作者： Trust, Paul Minghim, Rosane Kadusabe, Provia Zahran, Ahmed Younis, Haseeb Millos, Evangelos University College Cork Ireland Worldquant University United States Dalhousie University Canada

This paper describes our system for the submission to the TextGraphs 2022 shared task at COLING 2022: natural language Premise Selection (NLPS) from mathematical texts. The task of NLPS regards selecting mathematical statements called premises in a knowledge base written in natural language and mathematical formulae that are most likely to be used to achieve a particular mathematical proof. We formulated this solution as an unsupervised semantic similarity task by first obtaining contextualized embeddings of both the premises and mathematical proofs using sentence transformers. We then obtained the cosine similarity between these embeddings and then selected premises with the highest cosine scores as the most probable. Our system improves over the baseline system that uses bag of words models based on term frequency inverse document frequency in terms of mean average precision (MAP) by about 23.5% (0.1516 versus 0.1228). © 2022 Proceedings - International conference on Computational Linguistics, COLING. All rights reserved.

关键词： Embeddings

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Few-shot Learning for Slot Tagging with Attentive Relational Network 16

Few-shot Learning for Slot Tagging with Attentive Relational...

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16th conference of the European-Chapter-of-the-Association-for-Computational-Linguistics (EACL)

作者： Oguz, Cennet Vu, Ngoc Thang German Res Ctr Artificial Intelligence Multilingual & Language Technol Kaiserslautern Germany Univ Stuttgart Inst Nat Language Proc Stuttgart Germany

ISBN: (纸本)9781954085022

Metric-based learning is a well-known family of methods for few-shot learning, especially in computer vision. Recently, they have been used in many natural language processing applications but not for slot tagging. In this paper, we explore metric-based learning methods in the slot tagging task and propose a novel metric-based learning architecture - Attentive Relational Network. Our proposed method extends relation networks, making them more suitable for natural language processing applications in general, by leveraging pretrained contextual embeddings such as ELMO and BERT and by using attention mechanism. The results on SNIPS data show that our proposed method outperforms other state of the art metric-based learning methods.

关键词： Learning systems

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CogBERT: Cognition-Guided Pre-trained language Models 29

CogBERT: Cognition-Guided Pre-trained Language Models

引用

29th International conference on Computational Linguistics, COLING 2022

作者： Ding, Xiao Chen, Bowen Du, Li Qin, Bing Liu, Ting Harbin Institute of Technology China

We study the problem of integrating cognitive language processing signals (e.g., eyetracking or EEG data) into pre-trained models like BERT. Existing methods typically fine-tune pre-trained models on cognitive data, ignoring the semantic gap between the texts and cognitive signals. To fill the gap, we propose CogBERT, a framework that can induce fine-grained cognitive features from cognitive data and incorporate cognitive features into BERT by adaptively adjusting the weight of cognitive features for different NLP tasks. Extensive experiments show that: (1) Cognition-guided pre-trained models can consistently perform better than basic pre-trained models on ten NLP tasks. (2) Different cognitive features contribute differently to different NLP tasks. Based on this observation, we give a fine-grained explanation of why cognitive data is helpful for NLP. (3) Different transformer layers of pre-trained models should encode different cognitive features, with word-level cognitive features at the bottom and semantic-level cognitive features at the top. (4) Attention visualization demonstrates that CogBERT can align with human gaze patterns and improves its natural language understanding ability. © 2022 Proceedings - International conference on Computational Linguistics, COLING. All rights reserved.

关键词： natural language processing systems

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Global-Multiscale Channel Convolutional Network For Hyperspectral Image Classification

Global-Multiscale Channel Convolutional Network For Hyperspe...

引用

natural language processing (ICNLP), International conference on

作者： Yuquan Gan Hao Zhang Yixin Yang Chen Yi Xi’an University of Posts and Telecommunication School of Communication and Information Engineering Xi’an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

Convolutional neural networks (CNNs) have gained significant popularity in the field of hyperspectral image (HSI) classification due to their ability to capture detailed features. However, the existed CNN-based HSI classification methods primarily rely on patch-based learning approaches. These methods do not fully utilize global information and also require high computational resources. To address these issues, we propose a novel image-based global learning framework for HSI classification. Within this framework, we introduce a Global-Multiscale Channel Convolutional Network that effectively exploits the global and multiscale information from HSI. In addition, we propose a novel spatial attention module. The module can mitigate the decline in performance that occurs with an increase of the input patch size by capturing the homogeneous pixels in the input patch. Our experimental results, conducted on two real hyperspectral datasets, demonstrate that our method outperforms other related methods in terms of both efficiency and accuracy for HSI land cover classification.

关键词： Deep learning Accuracy Land surface natural language processing Convolutional neural networks IP networks Optimization Hyperspectral imaging Image classification

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ADT: An Additive Delta-Tuning approach for parameter-efficient tuning in pre-trained language models

ADT: An Additive Delta-Tuning approach for parameter-efficie...

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natural language processing (ICNLP), International conference on

作者： Dong Li Jintao Tang Shasha Li Ting Wang College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

The advent of pre-trained language models (PLMs) has revolutionized the field of natural language processing (NLP), enabling models to leverage vast amounts of language and world knowledge for various downstream tasks. However, the conventional fine-tuning of these models requires updating full parameters, which is computationally intensive and impractical in resource-constrained settings. Parameter-efficient tuning methods have been developed to mitigate this. However, these methods may need to be improved in terms of inference latency, optimization difficulty, and input sequence length *** this paper, we introduce a new reparameterization-based parameter-efficient tuning method, Additive Delta Tuning (ADT). ADT only fine-tunes part of the row and column parameters in the PLM weight matrix. This approach significantly reduces the computational cost compared to existing methods, such as Low-Rank Adaptation (LoRA), while maintaining competitive performance. We conducted extensive experiments across multiple natural language understanding (NLU) tasks, including text classification, text entailment, named entity recognition, etc., to evaluate the effectiveness of ADT. Our experimental results demonstrate that ADT achieves comparable or better performance than traditional fine-tuning methods, requiring less than 0.3% parameter updates. This shows that ADT is an effective method for parameter-efficient tuning. Our method achieves comparable results to LoRA on multiple NLU tasks, with the added benefit of requiring less computational cost.

关键词： Adaptation models Additives Computational modeling Text categorization Named entity recognition Rapid prototyping Stability analysis Computational efficiency Tuning Optimization

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1st International conference on Electronic Information Technology and Smart Agriculture, ICEITSA 2021

作者： Li, Tao Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781665413008

natural language processing as the current science and technology innovation and the main direction of artificial intelligence research and needs to be communication between man and machine as a fusion algorithm of computer science, linguistics, mathematics, and other the premise condition of theoretical knowledge, experience and combined with empirical research scholars at home and abroad to conduct a comprehensive discussion. This paper mainly starts from the convolutional neural network algorithm, and makes an in-depth study of Chinese entity relation extraction in natural language processing. Combined with empirical cases, the final results show that it is feasible to extract Chinese entity relation using convolutional neural network model. © 2021 IEEE.

关键词： Convolutional neural networks

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Enhancing Deep Learning-based Depression Level Estimation Based on Multi-task Learning 24

Enhancing Deep Learning-based Depression Level Estimation Ba...

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7th International conference on Machine Learning and Machine Intelligence, MLMI 2024

作者： Ye, Qingxin Xie, Zhenming Sun, Hao Xin, Luyao Chen, Youwen Song, Jian Chen, Yen-Wei Provincial key Laboratory of Computational Science Huaqiao University Quanzhou China Intelligent Image Processing Lab Zhejiang University Hangzhou China Intelligent Image Processing Lab Ritsumeikan University Osaka Japan

ISBN: (纸本)9798400717833

In recent years, depression, as a serious mental illness, has received widespread attention from various sectors of society. How to identify depressive emotions in a timely manner and detect depression has become an urgent issue that needs to be addressed. Currently, many experts and scholars propose using deep learning methods to assess the level of depression. Compared to other deep learning models, Transformer models have parallel computing capabilities with their self-attention mechanism, enabling them to handle entire sequences and capture long-distance dependencies. After the success of Transformers in natural language processing, similar architectures have been introduced into the field of computer vision, promoting advancements in multi-modal research. However, how to integrate other information or tasks to improve assessment accuracy remains a challenge. In our team's preliminary work, we proposed a multi-modal adaptive fusion Transformer network for assessing depression levels. Inspired by multi-task learning, we introduced gender classification as a second auxiliary task alongside the main task of depression regression, forming a three-task learning framework including depression classification. This was done to enhance the model's performance. We validated the effectiveness of the proposed method by testing it on a public dataset (AVEC 2019 Detecting Depression with AI Sub-challenge). The experimental results show that our proposed method achieved a Concordance Correlation Coefficient (CCC) of 0.60448, which is 1.688% higher than the accuracy of the dual-task learning method (CCC = 0.5876). © 2024 Copyright held by the owner/author(s).

关键词： Multi-task learning

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Sublinear Time Approximation of Text Similarity Matrices 36

Sublinear Time Approximation of Text Similarity Matrices

引用

36th AAAI conference on Artificial Intelligence / 34th conference on Innovative Applications of Artificial Intelligence / 12th Symposium on Educational Advances in Artificial Intelligence

作者： Ray, Archan Monath, Nicholas McCallum, Andrew Musco, Cameron Univ Massachusetts Amherst Amherst MA 01003 USA Google Mountain View CA 94043 USA

ISBN: (纸本)9781577358763

We study algorithms for approximating pairwise similarity matrices that arise in natural language processing. Generally, computing a similarity matrix for n data points requires Omega(n(2)) similarity computations. This quadratic scaling is a significant bottleneck, especially when similarities are computed via expensive functions, e.g., via transformer models. Approximation methods reduce this quadratic complexity, often by using a small subset of exactly computed similarities to approximate the remainder of the complete pairwise similarity matrix. Significant work focuses on the efficient approximation of positive semidefinite (PSD) similarity matrices, which arise e.g., in kernel methods. However, much less is understood about indefinite (non-PSD) similarity matrices, which often arise in NLP. Motivated by the observation that many of these matrices are still somewhat close to PSD, we introduce a generalization of the popular Nystrom method to the indefinite setting. Our algorithm can be applied to any similarity matrix and runs in sublinear time in the size of the matrix, producing a rank-8 approximation with just O(ns) similarity computations. We show that our method, along with a simple variant of CUR decomposition, performs very well in approximating a variety of similarity matrices arising in NLP tasks. We demonstrate high accuracy of the approximated similarity matrices in tasks of document classification, sentence similarity, and cross-document coreference.

关键词： natural language processing systems

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Decomposing natural Logic Inferences for Neural NLI 5

Decomposing Natural Logic Inferences for Neural NLI

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5th Workshop on Analyzing and Interpreting Neural Networks for NLP, BlackboxNLP 2022 hosted by the 2022 conference on empirical methods in natural language processing, EMNLP 2022

作者： Rozanova, Julia Ferreira, Deborah Thayaparan, Mokanarangan Valentino, Marco Freitas, André Department of Computer Science University of Manchester United Kingdom Idiap Research Institute Switzerland

ISBN: (纸本)9781959429050

In the interest of interpreting neural NLI models and their reasoning strategies, we carry out a systematic probing study which investigates whether these models capture the crucial semantic features central to natural logic: monotonicity and concept inclusion. Correctly identifying valid inferences in downward-monotone contexts is a known stumbling block for NLI performance, subsuming linguistic phenomena such as negation scope and generalized quantifiers. To understand this difficulty, we emphasize monotonicity as a property of a context and examine the extent to which models capture relevant monotonicity information in the vector representations which are intermediate to their decision making process. Drawing on the recent advancement of the probing paradigm, we compare the presence of monotonicity features across various models. We find that monotonicity information is notably weak in the representations of popular NLI models which achieve high scores on benchmarks, and observe that previous improvements to these models based on fine-tuning strategies have introduced stronger monotonicity features together with their improved performance on challenge sets. © 2022 Association for Computational Linguistics.

关键词： Benchmarking

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Present state and future scope of Assamese text processing

Present state and future scope of Assamese text processing

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11th International conference of Computer Communication and Informatics (ICCCI)

作者： Baishya, Diganta Baruah, Rupam Neog, Anupal Jorhat Engn Coll Dept CSE Jorhat Assam India

ISBN: (纸本)9781728158754

natural language processing has been a challenging area for researchers in recent times. A lot of research works are conducted to improve the performance of natural language processing that have helped many applications to be made available for our day to day life. Most of the developments have happened only for a few western languages. Researchers have also published some quality works related to Indian languages in recent times. However, the research works conducted for most of the other languages spoken in India, the country with the second largest population in the world, are at a very primitive stage. This paper aims to review the advances in this area for one of the Low Resource Indian languages -"Assamese", the official language of Assam, the gateway to North East India. It is also spoken in many other states of North Eastern part of India. In this work, we present a highlight of research works related to various methods that are applied to Assamese Text processing. It is observed that certain language characteristics of Assamese are best applicable to certain popular methods used for natural language processing. The reason behind this relook is to overview and report the present state and future possibilities of research in text processing in Assamese. This paper should serve as a good beginning for anyone interested to carry forward the computational works for Assamese. We conclude the paper with a brief discussion on the scope of Assamese Text processing in the future.

关键词： natural language processing Assamese Indian language survey paper text processing

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