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

共 15208 条记录，以下是4971-4980 订阅

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iSentenizer: An incremental sentence boundary classifier

iSentenizer: An incremental sentence boundary classifier

引用

International conference on natural language processing and Knowledge Engineering

作者： Wong, Fai Chao, Sam CIS University of Macau China

ISBN: (纸本)9781424468966

In this paper, we revisited the topic of sentence boundary detection, and proposed an incremental approach to tackle the problem. The boundary classifier is revised on the fly to adapt to the text of high variety of sources and genres. We applied i+ Learning, an incremental algorithm, for constructing the sentence boundary detection model using different features based on local context. Although the model can be easily trained on any genre of text and on any alphabet language, we emphasize the ability that the classifier is adaptable to text with domain and topic shifts without retraining the whole model from scratch. empirical results indicate that the performance of proposed system is comparable to that of similar systems. ©2010 IEEE.

关键词： alphabet language computational linguistics i+Learning i+Learning iSentenizer incremental learning incremental sentence boundary classifier learning (artificial intelligence) pattern classification sentence boundary detection sentence boundary detection tagging text analysis

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Detailed Study of Deep Learning Models for natural language processing 2

Detailed Study of Deep Learning Models for Natural Language ...

引用

2nd IEEE International conference on Advances in Computing, Communication Control and Networking, ICACCCN 2020

作者： Gupta, Megha Verma, Shailesh Kumar Jain, Priyanshu Guru Gobind Singh Indraprastha University University School of Information Communication and Technology New Delhi India

ISBN: (纸本)9781728183374

natural language processing involves computational processing, and understanding of human languages. With the increase in computation power, deep learning models are being used for various NLP tasks. Further availability of large datasets of various languages enables the training of deep learning models. Multiple processing layers are used by the deep learning methods for learning representations of data which are hierarchical in nature and which gives excellent results for different NLP tasks. This paper reviews the important models and methods in deep learning which are applied to natural language problems. In particular, Convolutional Neural Network, Recurrent Neural Network, Long Short-Term Memory, Gated Recurrent Unit, Recursive Neural Network have been described. Also, their advantages and suitability to various natural language processing applications such as text classification, sentiment analysis, etc. have been reviewed. © 2020 IEEE.

关键词： Sentiment analysis

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INTENTIONQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of language Models in E-commerce

INTENTIONQA: A Benchmark for Evaluating Purchase Intention C...

引用

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

作者： Ding, Wenxuan Wang, Weiqi Kwok, Sze Heng Douglas Liu, Minghao Fang, Tianqing Bai, Jiaxin Liu, Xin Yu, Changlong Li, Zheng Luo, Chen Yin, Qingyu Yin, Bing He, Junxian Song, Yangqiu Department of Computer Science and Engineering HKUST Hong Kong *** Inc. Palo AltoCA United States

ISBN: (纸本)9798891761681

Enhancing language Models' (LMs) ability to understand purchase intentions in E-commerce scenarios is crucial for their effective assistance in various downstream tasks. However, previous approaches that distill intentions from LMs often fail to generate meaningful and human-centric intentions applicable in real-world E-commerce contexts. This raises concerns about the true comprehension and utilization of purchase intentions by LMs. In this paper, we present INTENTIONQA, a double-task multiple-choice question answering benchmark to evaluate LMs' comprehension of purchase intentions in E-commerce. Specifically, LMs are tasked to infer intentions based on purchased products and utilize them to predict additional purchases. INTENTIONQA consists of 4,360 carefully curated problems across three difficulty levels, constructed using an automated pipeline to ensure scalability on large E-commerce services. Human evaluations demonstrate the high quality and low false-negative rate of our benchmark. Extensive experiments across 19 language models show that they still struggle with certain scenarios, such as understanding products and intentions accurately, jointly reasoning with products and intentions, and more, in which they fall far behind human performances. Our code and data are publicly available at https://***/HKUSTKnowComp/IntentionQA. © 2024 Association for Computational Linguistics.

关键词： Sales

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MemPrompt: Memory-assisted Prompt Editing with User Feedback

MemPrompt: Memory-assisted Prompt Editing with User Feedback

引用

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

作者： Madaan, Aman Tandon, Niket Clark, Peter Yang, Yiming Language Technologies Institute Carnegie Mellon University PittsburghPA United States Allen Institute for Artificial Intelligence SeattleWA United States

Large LMs such as GPT-3 are powerful, but can commit mistakes that are obvious to humans. For example, GPT-3 would mistakenly interpret "What word is similar to good?" to mean a homophone, while the user intended a synonym. Our goal is to effectively correct such errors via user interactions with the system but without retraining, which will be prohibitively costly. We pair GPT-3 with a growing memory of recorded cases where the model misunderstood the user's intents, along with user feedback for clarification. Such a memory allows our system to produce enhanced prompts for any new query based on the user feedback for error correction on similar cases in the past. On four tasks (two lexical tasks, two advanced ethical reasoning tasks), we show how a (simulated) user can interactively teach a deployed GPT-3, substantially increasing its accuracy over the queries with different kinds of misunderstandings by the GPT-3. Our approach is a step towards the low-cost utility enhancement for very large pre-trained LMs. © 2022 Association for Computational Linguistics.

关键词： Error correction

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ConTReGen: Context-driven Tree-structured Retrieval for Open-domain Long-form Text Generation

ConTReGen: Context-driven Tree-structured Retrieval for Open...

引用

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

作者： Roy, Kashob Kumar Akash, Pritom Saha Chang, Kevin Chen-Chuan Popa, Lucian University of Illinois Urbana-Champaign United States IBM Research United States

ISBN: (纸本)9798891761681

Open-domain long-form text generation requires generating coherent, comprehensive responses that address complex queries with both breadth and depth. This task is challenging due to the need to accurately capture diverse facets of input queries. Existing iterative retrieval-augmented generation (RAG) approaches often struggle to delve deeply into each facet of complex queries and integrate knowledge from various sources effectively. This paper introduces ConTReGen, a novel framework that employs a context-driven, tree-structured retrieval approach to enhance the depth and relevance of retrieved content. ConTReGen integrates a hierarchical, top-down in-depth exploration of query facets with a systematic bottom-up synthesis, ensuring comprehensive coverage and coherent integration of multifaceted information. Extensive experiments on multiple datasets, including LFQA and ODSUM, alongside a newly introduced dataset, ODSUM-WikiHow, demonstrate that ConTReGen outperforms existing state-of-the-art RAG models. © 2024 Association for Computational Linguistics.

关键词： Structured Query language

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SLING: Sino LINGuistic Evaluation of Large language Models

SLING: Sino LINGuistic Evaluation of Large Language Models

引用

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

作者： Song, Yixiao Krishna, Kalpesh Bhatt, Rajesh Iyyer, Mohit Department of Linguistics UMass Amherst United States Manning College of Information and Computer Sciences UMass Amherst United States

To understand what kinds of linguistic knowledge are encoded by pretrained Chinese language models (LMs), we introduce the benchmark of Sino LINGuistics (SLING), which consists of 38K minimal sentence pairs in Mandarin Chinese grouped into 9 high-level linguistic phenomena. Each pair demonstrates the acceptability contrast of a specific syntactic or semantic phenomenon (e.g., The keys are lost vs. The keys is lost), and an LM should assign lower perplexity to the acceptable sentence. In contrast to the CLiMP dataset (Xiang et al., 2021), which also contains Chinese minimal pairs and was created by translating the vocabulary of the English BLiMP dataset, the minimal pairs in SLING are derived primarily by applying syntactic and lexical transformations to naturally-occurring, linguist-annotated sentences from the Chinese Treebank 9.0, thus addressing severe issues in CLiMP's data generation process. We test 18 publicly available pretrained monolingual (e.g., BERT-base-zh, CPM) and multi-lingual (e.g., mT5, XLM) language models on SLING. Our experiments show that the average accuracy for LMs is far below human performance (69.7% vs. 97.1%), while BERT-base-zh achieves the highest accuracy (84.8%) of all tested LMs, even much larger ones. Additionally, we find that most LMs have a strong gender and number (singular/plural) bias, and they perform better on local phenomena than hierarchical ones. © 2022 Association for Computational Linguistics.

关键词： Syntactics

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Unsupervised Classification of Sentiment and Objectivity in Chinese Text 3

引用

3rd International Joint conference on natural language processing, IJCNLP 2008

作者： Zagibalov, Taras Carroll, John Department of Informatics University of Sussex BrightonBN1 9QH United Kingdom

ISBN: (纸本)9780000000002

We address the problem of sentiment and objectivity classification of product reviews in Chinese. Our approach is distinctive in that it treats both positive / negative sentiment and subjectivity / objectivity not as distinct classes but rather as a continuum;we argue that this is desirable from the perspective of would-be customers who read the reviews. We use novel unsupervised techniques, including a one-word 'seed' vocabulary and iterative retraining for sentiment processing, and a criterion of 'sentiment density' for determining the extent to which a document is opinionated. The classifier achieves up to 87% F-measure for sentiment polarity detection. © 2008 IJCNLP 2008 - 3rd International Joint conference on natural language processing, Proceedings of the conference. All rights reserved.

关键词： Iterative methods

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Prompt-and-Rerank: A Method for Zero-Shot and Few-Shot Arbitrary Textual Style Transfer with Small language Models

Prompt-and-Rerank: A Method for Zero-Shot and Few-Shot Arbit...

引用

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

作者： Suzgun, Mirac Melas-Kyriazi, Luke Jurafsky, Dan Stanford University United States Oxford University United Kingdom

We propose a method for arbitrary textual style transfer (TST)-the task of transforming a text into any given style-utilizing general-purpose pre-trained language models. Our method, Prompt-and-Rerank, is based on a mathematical formulation of the TST task, decomposing it into three constituent components: textual similarity, target style strength, and fluency. Our method uses zero-shot or few-shot prompting to obtain a set of candidate generations in the target style, and then re-ranks them according to the three components. Our method enables small pre-trained language models to perform on par with state-of-the-art large-scale models while using two orders of magnitude less compute and memory. We also investigate the effect of model size and prompt design (e.g., prompt paraphrasing and delimiter-pair choice) on style transfer quality across seven diverse textual style transfer datasets, finding, among other things, that delimiter-pair choice has a large impact on performance, and that models have biases on the direction of style transfer. © 2022 Association for Computational Linguistics.

关键词： Large dataset

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SSP: Self-Supervised Prompting for Cross-Lingual Transfer to Low-Resource languages using Large language Models

SSP: Self-Supervised Prompting for Cross-Lingual Transfer to...

引用

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

作者： Rathore, Vipul Deb, Aniruddha Chandresh, Ankish Singla, Parag Mausam Indian Institute of Technology New Delhi India

ISBN: (纸本)9798891761681

Recently, very large language models (LLMs) have shown exceptional performance on several English NLP tasks with just in-context learning (ICL), but their utility in other languages is still underexplored. We investigate their effectiveness for NLP tasks in low-resource languages (LRLs), especially in the setting of zero-labeled cross-lingual transfer (0-CLT), where no labeled training data exists for the target language but data from related medium-resource languages (MRLs) and unlabeled test data for the target language are available. We introduce Self-Supervised Prompting (SSP), a novel ICL approach tailored for the 0-CLT setting. SSP leverages the key observation that LLMs output more accurate labels if in-context exemplars are given from the target language, even if their labels are slightly noisy. To operationalize this, since target language training data is not available in 0-CLT setup, SSP operates in two stages. In Stage I, using source MRL training data, target language's test data is noisily labeled. In Stage II, these noisy test data points are used as exemplars in ICL for further improved labeling. Additionally, our implementation of SSP uses a novel Integer Linear Programming (ILP)-based exemplar selection method that balances similarity, prediction confidence and label coverage. Experimental results on three tasks and eleven LRLs (from three regions) demonstrate that SSP strongly outperforms existing SOTA fine-tuned and prompting-based baselines in the 0-CLT setting. © 2024 Association for Computational Linguistics.

关键词： Integer programming

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When "A Helpful Assistant" Is Not Really Helpful: Personas in System Prompts Do Not Improve Performances of Large language Models

When "A Helpful Assistant" Is Not Really Helpful: Personas i...

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

作者： Zheng, Mingqian Pei, Jiaxin Logeswaran, Lajanugen Lee, Moontae Jurgens, David Carnegie Mellon University United States Stanford University United States LG AI Research Korea Republic of University of Illinois Chicago United States University of Michigan United States

ISBN: (纸本)9798891761681

Prompting serves as the major way humans interact with Large language Models (LLM). Commercial AI systems commonly define the role of the LLM in system prompts. For example, ChatGPT uses "You are a helpful assistant" as part of its default system prompt. Despite current practices of adding personas to system prompts, it remains unclear how different personas affect a model's performance on objective tasks. In this study, we present a systematic evaluation of personas in system prompts. We curate a list of 162 roles covering 6 types of interpersonal relationships and 8 domains of expertise. Through extensive analysis of 4 popular families of LLMs and 2,410 factual questions, we demonstrate that adding personas in system prompts does not improve model performance across a range of questions compared to the control setting where no persona is added. Nevertheless, further analysis suggests that the gender, type, and domain of the persona can all influence the resulting prediction accuracies. We further experimented with a list of persona search strategies and found that, while aggregating results from the best persona for each question significantly improves prediction accuracy, automatically identifying the best persona is challenging, with predictions often performing no better than random selection. Overall, our findings suggest that while adding a persona may lead to performance gains in certain settings, the effect of each persona can be largely random. Code and data are available at https://***/Jiaxin-Pei/Prompting-with-Social-Roles. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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