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

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Phrase Composing Tool using natural language processing

Phrase Composing Tool using Natural Language Processing

引用

2021 International conference on Intelligent Technologies, CONIT 2021

作者： Ahirrao, Mayur Joshi, Yash Gandhe, Atharva Kotgire, Sumeet Deshmukh, Rohini G. Sinhgad Academy of Engineering Computer Department Kondhwa411048 India

ISBN: (纸本)9781728185835

In this fast-running world, machine communication plays a vital role. To compete with this world, human-machine interaction is a necessary thing. To enhance this, natural language processing technique is used widely. Using this technique, we can reduce the interaction gap between the machine and human. Till now, many such applications are developed which are using this *** tool deals with the various methods which are used for development of grammar error correction. These methods include rule-based method, classifier-based method and machine translation-based method. Also, models regarding the natural language processing (NLP) pipeline are trained and implemented in this project accordingly. Additionally, the tool can also perform speech to text operation. © 2021 IEEE.

关键词： Error correction

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Making Pre-trained language Models Better Few-shot Learners 59

Making Pre-trained Language Models Better Few-shot Learners

引用

Joint conference of 59th Annual Meeting of the Association-for-Computational-Linguistics (ACL) / 11th International Joint conference on natural language processing (IJCNLP) / 6th Workshop on Representation Learning for NLP (RepL4NLP)

作者： Gao, Tianyu Fisch, Adam Chen, Danqi Princeton Univ Princeton NJ 08544 USA MIT 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9781954085527

The recent GPT-3 model (Brown et al., 2020) achieves remarkable few-shot performance solely by leveraging a natural-language prompt and a few task demonstrations as input context. Inspired by their findings, we study few-shot learning in a more practical scenario, where we use smaller language models for which fine-tuning is computationally efficient. We present LM-BFF-better few-shot fine-tuning of language models1-a suite of simple and complementary techniques for fine-tuning language models on a small number of annotated examples. Our approach includes (1) prompt-based fine-tuning together with a novel pipeline for automating prompt generation;and (2) a refined strategy for dynamically and selectively incorporating demonstrations into each context. Finally, we present a systematic evaluation for analyzing few-shot performance on a range of NLP tasks, including classification and regression. Our experiments demonstrate that our methods combine to dramatically outperform standard fine-tuning procedures in this low resource setting, achieving up to 30% absolute improvement, and 11% on average across all tasks. Our approach makes minimal assumptions on task resources and domain expertise, and hence constitutes a strong task-agnostic method for few-shot learning.(2)

关键词： Computational linguistics

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Graphdocexplore: A framework for the experimental comparison of graph-based document exploration techniques

Graphdocexplore: A framework for the experimental comparison...

引用

2017 conference on empirical methods in natural language processing: System Demonstrations, EMNLP 2017

作者： Falke, Tobias Gurevych, Iryna Research Training Group AIPHES and UKP Lab Department of Computer Science Technische Universität Darmstadt Germany

ISBN: (纸本)9781945626975

Graphs have long been proposed as a tool to browse and navigate in a collection of documents in order to support exploratory search. Many techniques to automatically extract different types of graphs, showing for example entities or concepts and different relationships between them, have been suggested. While experimental evidence that they are indeed helpful exists for some of them, it is largely unknown which type of graph is most helpful for a specific exploratory task. However, carrying out experimental comparisons with human subjects is challenging and time-consuming. Towards this end, we present the GraphDocExplore framework. It provides an intuitive web interface for graph-based document exploration that is optimized for experimental user studies. Through a generic graph interface, different methods to extract graphs from text can be plugged into the system. Hence, they can be compared at minimal implementation effort in an environment that ensures controlled comparisons. The system is publicly available under an open-source license.1. © 2017 Association for Computational Linguistics.

关键词： Graphic methods

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A dynamic associative semantic model for natural language processing based on a spreading activation network 20

A dynamic associative semantic model for natural language pr...

引用

XXth International conference of the Chilean-Computer-Science-Society (SCCC 2000)

作者： Bassi, A Univ Chile Fac Ciencias Fis & Matemat Dept Ciencias Computac Santiago Chile

ISBN: (纸本)0769508103

This paper presents a semantic model based on well-known psycholinguistic theories of human memory. It is centered on a spreading activation network, but it departs from classical models by representing associations between structured units instead of atomic nodes. Network units have an activity level that evolves according to their expected contextual relevance. Spreading activation explains the predictive top-down effect of knowledge. It supports a general heuristics which may be used as the first step of more elaborated methods. This model is suited to deal with the interaction between semantic and episodic memories, as well as many other practical issues regarding natural language processing, including the retroactive effect of semantics over perception and the operation in open-worlds.

关键词： Computational linguistics Computational modeling Computer networks Electronic mail Humans Knowledge representation Logic design natural language processing natural languages Psychology

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Measuring Context-Word Biases in Lexical Semantic Datasets

Measuring Context-Word Biases in Lexical Semantic Datasets

引用

2022 conference on empirical methods in natural language processing, EMNLP 2022

作者： Liu, Qianchu McCarthy, Diana Korhonen, Anna Language Technology Lab TAL University of Cambridge United Kingdom

State-of-the-art pretrained contextualized models (PCM) eg. BERT use tasks such as WiC and WSD to evaluate their word-in-context representations. This inherently assumes that performance in these tasks reflect how well a model represents the coupled word and context semantics. We question this assumption by presenting the first quantitative analysis on the context-word interaction being tested in major contextual lexical semantic tasks. To achieve this, we run probing baselines on masked input, and propose measures to calculate and visualize the degree of context or word biases in existing datasets. The analysis was performed on both models and humans. Our findings demonstrate that models are usually not being tested for word-in-context semantics in the same way as humans are in these tasks, which helps us better understand the model-human gap. Specifically, to PCMs, most existing datasets fall into the extreme ends (the retrieval-based tasks exhibit strong target word bias while WiC-style tasks and WSD show strong context bias);In comparison, humans are less biased and achieve much better performance when both word and context are available than with masked input. We recommend our framework for understanding and controlling these biases for model interpretation and future task design. © 2022 Association for Computational Linguistics.

关键词： Semantics

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Granularity is crucial when applying differential privacy to text: An investigation for neural machine translation

Granularity is crucial when applying differential privacy to...

引用

2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Vu, Doan Nam Long Igamberdiev, Timour Habernal, Ivan Trustworthy Human Language Technologies Germany Department of Computer Science Technical University of Darmstadt Germany Research Center Trustworthy Data Science and Security of the University Alliance Ruhr Faculty of Computer Science Ruhr University Bochum Germany

ISBN: (纸本)9798891761681

Applying differential privacy (DP) by means of the DP-SGD algorithm to protect individual data points during training is becoming increasingly popular in NLP. However, the choice of granularity at which DP is applied is often neglected. For example, neural machine translation (NMT) typically operates on the sentence-level granularity. From the perspective of DP, this setup assumes that each sentence belongs to a single person and any two sentences in the training dataset are independent. This assumption is however violated in many real-world NMT datasets, e.g., those including dialogues. For proper application of DP we thus must shift from sentences to entire documents. In this paper, we investigate NMT at both the sentence and document levels, analyzing the privacy/utility trade-off for both scenarios, and evaluating the risks of not using the appropriate privacy granularity in terms of leaking personally identifiable information (PII). Our findings indicate that the document-level NMT system is more resistant to membership inference attacks, emphasizing the significance of using the appropriate granularity when working with DP. © 2024 Association for Computational Linguistics.

关键词： Differential privacy

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Medical Adaptation of Large language and Vision-language Models: Are We Making Progress?

Medical Adaptation of Large Language and Vision-Language Mod...

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2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Jeong, Daniel P. Garg, Saurabh Lipton, Zachary C. Oberst, Michael Machine Learning Department Carnegie Mellon University United States Mistral AI France Department of Computer Science Johns Hopkins University United States Abridge AI United States

ISBN: (纸本)9798891761643

Several recent works seek to develop foundation models specifically for medical applications, adapting general-purpose large language models (LLMs) and vision-language models (VLMs) via continued pretraining on publicly available biomedical corpora. These works typically claim that such domain-adaptive pretraining (DAPT) improves performance on downstream medical tasks, such as answering medical licensing exam questions. In this paper, we compare seven public "medical" LLMs and two VLMs against their corresponding base models, arriving at a different conclusion: all medical VLMs and nearly all medical LLMs fail to consistently improve over their base models in the zero-/few-shot prompting regime for medical question-answering (QA) tasks. For instance, across the tasks and model pairs we consider in the 3-shot setting, medical LLMs only outperform their base models in 12.1% of cases, reach a (statistical) tie in 49.8% of cases, and are significantly worse than their base models in the remaining 38.2% of cases. Our conclusions are based on (i) comparing each medical model head-to-head, directly against the corresponding base model;(ii) optimizing the prompts for each model separately;and (iii) accounting for statistical uncertainty in comparisons. While these basic practices are not consistently adopted in the literature, our ablations show that they substantially impact conclusions. Our findings suggest that state-of-the-art general-domain models may already exhibit strong medical knowledge and reasoning capabilities, and offer recommendations to strengthen the conclusions of future studies. © 2024 Association for Computational Linguistics.

关键词： Visual languages

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Long-Form Speech Translation through Segmentation with Finite-State Decoding Constraints on Large language Models

Long-Form Speech Translation through Segmentation with Finit...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： McCarthy, Arya D. Zhang, Hao Kumar, Shankar Stahlberg, Felix Wu, Ke Johns Hopkins Univ Ctr Language & Speech Proc Baltimore MD 21218 USA Google Res Mountain View CA USA

ISBN: (纸本)9798891760615

One challenge in speech translation is that plenty of spoken content is long-form, but short units are necessary for obtaining high-quality translations. To address this mismatch, we adapt large language models (LLMs) to split long ASR transcripts into segments that can be independently translated so as to maximize the overall translation quality. We overcome the tendency of hallucination in LLMs by incorporating finite-state constraints during decoding;these eliminate invalid outputs without requiring additional training. We discover that LLMs are adaptable to transcripts containing ASR errors through prompt-tuning or fine-tuning. Relative to a state-of-the-art automatic punctuation baseline, our best LLM improves the average BLEU by 2.9 points for English-German, English-Spanish, and English-Arabic TED talk translation in 9 test sets, just by improving segmentation.

关键词： Decoding

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Detection of Phishing in Mobile Instant Messaging using natural language processing and Machine Learning 11

Detection of Phishing in Mobile Instant Messaging using Natu...

引用

11th International conference in Software Engineering Research and Innovation (CONISOFT)

作者： Verma, Suman Ayala-Rivera, Vanessa Portillo-Dominguez, A. Omar Natl Coll Ireland Sch Comp Dublin Ireland Technol Univ Dublin Sch Business Technol Retail & Supply Chain Dublin Ireland

ISBN: (纸本)9798350328837;9798350328844

Advancements in mobile technology makes it easier to communicate in real time, but at the cost of having a wider potential attack area for phishing. While there has been research in the field related to Email and SMS, Instant Messages lags behind. The widespread usage of instant messengers by individuals of all ages further motivates the addition of software security features in this context. This research aims to detect phishing in mobile instant messages by analysing the language of the message with the help of natural language processing to detect keywords pointing towards phishing. We built the machine learning models using 3 different methods for feature extraction and 3 classification algorithms. Our tests showed that balancing the data with random oversampling increased the classifiers' performance, which were able to achieve an accuracy up to 99.2%.

关键词： Instant Messaging Social Engineering Phishing natural language processing Secure Software Engineering

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AUTOHALLUSION: Automatic Generation of Hallucination Benchmarks for Vision-language Models

AUTOHALLUSION: Automatic Generation of Hallucination Benchma...

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2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Wu, Xiyang Guan, Tianrui Li, Dianqi Huang, Shuaiyi Liu, Xiaoyu Wang, Xijun Xian, Ruiqi Shrivastava, Abhinav Huang, Furong Boyd-Graber, Jordan Lee Zhou, Tianyi Manocha, Dinesh University of Maryland College Park United States

ISBN: (纸本)9798891761681

Large vision-language models (LVLMs) are prone to hallucinations, where certain contextual cues in an image can trigger the language module to produce overconfident and incorrect reasoning about abnormal or hypothetical *** some benchmarks have been developed to investigate LVLM hallucinations, they often rely on hand-crafted corner cases whose failure patterns may not generalize ***, fine-tuning on these examples could undermine their *** address this, we aim to scale up the number of cases through an automated approach, reducing human bias in crafting such corner *** motivates the development of AUTOHALLUSION, the first automated benchmark generation approach that employs several key strategies to create a diverse range of hallucination *** generated visual-question pairs pose significant challenges to LVLMs, requiring them to overcome contextual biases and distractions to arrive at correct *** enables us to create new benchmarks at the minimum cost and thus overcomes the fragility of hand-crafted *** also reveals common failure patterns and reasons, providing key insights to detect, avoid, or control *** evaluations of top-tier LVLMs, e.g., GPT-4V(ision), Gemini Pro Vision, Claude 3, and LLaVA-1.5, show a 97.7% and 98.7% success rate of hallucination induction on synthetic and real-world datasets of AUTOHALLUSION, paving the way for a long battle against *** codebase and data can be accessed at https://***/wuxiyang1996/AutoHallusion. © 2024 Association for Computational Linguistics.

关键词： Visual languages

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