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A Comparative Assessment of State-Of-The-Art methods for Multilingual Unsupervised Keyphrase Extraction 17th

A Comparative Assessment of State-Of-The-Art Methods for Mul...

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17th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations (AIAI) / 6th 5G-PINE workshop / AI-BIO workshop / DAAI workshop / DARE workshop / EEAI workshop / 10th MHDW workshop

作者： Giarelis, Nikolaos Kanakaris, Nikos Karacapilidis, Nikos Univ Patras Ind Management & Informat Syst Lab MEAD Rion 26504 Greece

ISBN: (纸本)9783030791520;9783030791506;9783030791490

Keyphrase extraction is a fundamental task in information management, which is often used as a preliminary step in various information retrieval and natural language processing tasks. The main contribution of this paper lies in providing a comparative assessment of prominent multilingual unsupervised keyphrase extraction methods that build on statistical (RAKE, YAKE), graph-based (TextRank, SingleRank) and deep learning (KeyBERT) methods. For the experimentations reported in this paper, we employ well-known datasets designed for keyphrase extraction from five different natural languages (English, French, Spanish, Portuguese and Polish). We use the F1 score and a partial match evaluation framework, aiming to investigate whether the number of terms of the documents and the language of each dataset affect the accuracy of the selected methods. Our experimental results reveal a set of insights about the suitability of the selected methods in texts of different sizes, as well as the performance of these methods in datasets of different languages.

关键词： natural language processing Keyphrase extraction Unsupervised learning Deep learning graph-based models Empirical research

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Anchor-based Large language Models 62

Anchor-based Large Language Models

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62nd Annual Meeting of the Association-for-Computational-Linguistics (ACL) / Student Research workshop (SRW)

作者： Pang, Jianhui Ye, Fanghua Wong, Derek Fai He, Xin Chen, Wanshun Wang, Longyue Univ Macau Taipa Macao Peoples R China UCL London England Tencent AI Lab Shenzhen Peoples R China

ISBN: (纸本)9798891760998

Large language models (LLMs) predominantly employ decoder-only transformer architectures, necessitating the retention of keys/values information for historical tokens to provide contextual information and avoid redundant computation. However, the substantial size and parameter volume of these LLMs require massive GPU memory. This memory demand increases with the length of the input text, leading to an urgent need for more efficient methods of information storage and processing. This study introduces Anchor-based LLMs (AnLLMs), which utilize an innovative anchor-based self-attention network (AnSAN) and also an anchor-based inference strategy. This approach enables LLMs to compress sequence information into an anchor token, reducing the keys/values cache and enhancing inference efficiency. Experiments on question-answering benchmarks reveal that AnLLMs maintain similar accuracy levels while achieving up to 99% keys/values cache reduction and up to 3.5 times faster inference. Despite a minor compromise in accuracy, the substantial enhancements of AnLLMs employing the AnSAN technique in resource utilization and computational efficiency underscore their potential for practical LLM applications.(1)

关键词： Computational linguistics

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GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretation

GPT4Rec: A Generative Framework for Personalized Recommendat...

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2023 SIGIR workshop on eCommerce, eCom 2023

作者： Li, Jinming Zhang, Wentao Wang, Tian Xiong, Guanglei Lu, Alan Medioni, Gerard University of Michigan Ann Arbor United States Amazon United States

Recent advancements in natural language processing (NLP) have led to the development of NLP-based recommender systems that have shown superior performance. However, current models commonly treat items as mere IDs and adopt discriminative modeling, resulting in limitations of (1) fully leveraging the content information of items and the language modeling capabilities of NLP models;(2) interpreting user interests to improve relevance and diversity;and (3) adapting practical circumstances such as growing item inventories. To address these limitations, we present GPT4Rec, a novel and flexible generative framework inspired by search engines. It first generates hypothetical "search queries" given item titles in a user’s history, and then retrieves items for recommendation by searching these queries. The framework overcomes previous limitations by learning both user and item embeddings in the language space. To well-capture user interests with different aspects and granularity for improving relevance and diversity, we propose a multi-query generation technique with beam search. The generated queries naturally serve as interpretable representations of user interests and can be searched to recommend cold-start items. With GPT-2 language model and BM25 search engine, our framework outperforms state-of-the-art methods by 75.7% and 22.2% in Recall@K on two public datasets. Experiments further revealed that multi-query generation with beam search improves both the diversity of retrieved items and the coverage of a user’s multi-interests. The adaptiveness and interpretability of generated queries are discussed with qualitative case studies. © 2023 Copyright for this paper by its authors.

关键词： generative language models personalized recommendation query generation searching user interests modeling

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Understanding Domain Learning in language Models Through Subpopulation Analysis 5

Understanding Domain Learning in Language Models Through Sub...

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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： Zhao, Zheng Ziser, Yftah Cohen, Shay B. Institute for Language Cognition and Computation School of Informatics University of Edinburgh 10 Crichton Street EdinburghEH8 9AB United Kingdom

ISBN: (纸本)9781959429050

We investigate how different domains are encoded in modern neural network architectures. We analyze the relationship between natural language domains, model size, and the amount of training data used. The primary analysis tool we develop is based on subpopulation analysis with Singular Vector Canonical Correlation Analysis (SVCCA), which we apply to Transformer-based language models (LMs). We compare the latent representations of such a language model at its different layers from a pair of models: a model trained on multiple domains (an experimental model) and a model trained on a single domain (a control model). Through our method, we find that increasing the model capacity impacts how domain information is stored in upper and lower layers differently. In addition, we show that larger experimental models simultaneously embed domain-specific information as if they were conjoined control models. These findings are confirmed qualitatively, demonstrating the validity of our method. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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Evaluating Content Features and Classification methods for Helpfulness Prediction of Online Reviews: Establishing a Benchmark for Portuguese 12

Evaluating Content Features and Classification Methods for H...

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12th workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA 2022

作者： de Sousa, Rogério Figueredo Pardo, Thiago Alexandre Salgueiro Institute of Mathematical and Computer Sciences University of São Paulo Av. Trabalhador São Carlense 400 – SP São Carlos13.566-590 Brazil

ISBN: (纸本)9781955917520

Over the years, the review helpfulness prediction task has been the subject of several works, but remains being a challenging issue in natural language processing, as results vary a lot depending on the domain, on the adopted features and on the chosen classification strategy. This paper attempts to evaluate the impact of content features and classification methods for two different domains. In particular, we run our experiments for a low resource language – Portuguese –, trying to establish a benchmark for this language. We show that simple features and classical classification methods are powerful for the task of helpfulness prediction, but are largely outperformed by a convolutional neural network-based solution. © 2022 Association for Computational Linguistics.

关键词： Classification (of information)

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Bootstrapping Large-Scale Fine-Grained Contextual Advertising Classifier from Wikipedia 15

Bootstrapping Large-Scale Fine-Grained Contextual Advertisin...

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15th workshop on graph-based methods for natural language processing, Textgraphs 2021

作者： Jin, Yiping Kadam, Vishakha Wanvarie, Dittaya Knorex 140 Robinson Road 14-16 Crown @ Robinson Singapore Department of Mathematics & Computer Science Chulalongkorn University Thailand

ISBN: (纸本)9781954085381

Contextual advertising provides advertisers with the opportunity to target the context which is most relevant to their ads. The large variety of potential topics makes it very challenging to collect training documents to build a supervised classification model or compose expert-written rules in a rule-based classification system. Besides, in fine-grained classification, different categories often overlap or cooccur, making it harder to classify accurately. In this work, we propose wiki2cat, a method to tackle large-scaled fine-grained text classification by tapping on the Wikipedia category graph. The categories in the IAB taxonomy are first mapped to category nodes in the graph. Then the label is propagated across the graph to obtain a list of labeledWikipedia documents to induce text classifiers. The method is ideal for large-scale classification problems since it does not require any manually-labeled document or hand-curated rules or keywords. The proposed method is benchmarked with various learning-based and keyword-based baselines and yields competitive performance on publicly available datasets and a new dataset containing more than 300 fine-grained categories. © 2021 Association for Computational Linguistics.

关键词： Text processing

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Using NLP to Support English Teaching in Rural Schools 2

Using NLP to Support English Teaching in Rural Schools

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2nd workshop on NLP for Positive Impact, NLP4PI 2022 held in conjunction with the 2022 Conference on Empirical methods in natural language processing, EMNLP 2022

作者： Chiruzzo, Luis Musto, Laura Góngora, Santiago Carpenter, Brian Filevich, Juan Pablo Rosá, Aiala Universidad de la República Montevideo Uruguay Indiana University of Pennsylvania IndianaPA United States

ISBN: (纸本)9781959429197

We present a web application for creating games and exercises for teaching English as a foreign language with the help of NLP tools. The application contains different kinds of games such as crosswords, word searches, a memory game, and a multiplayer game based on the classic battleship pen and paper game. This application was built with the aim of supporting teachers in rural schools that are teaching English lessons, so they can easily create interactive and engaging activities for their students. We present the context and history of the project, the current state of the web application, and some ideas on how we will expand it in the future. © 2022 Association for Computational Linguistics.

关键词： natural language processing systems

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NLP4PI 2022 - 2nd workshop on NLP for Positive Impact, proceedings of the workshop

NLP4PI 2022 - 2nd Workshop on NLP for Positive Impact, Proce...

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2nd workshop on NLP for Positive Impact, NLP4PI 2022 held in conjunction with the 2022 Conference on Empirical methods in natural language processing, EMNLP 2022

ISBN: (纸本)9781959429197

The proceedings contain 17 papers. The topics discussed include: a unified framework for cross-domain and cross-task learning of mental health conditions;critical perspectives: a benchmark revealing pitfalls in PerspectiveAPI;securely capturing people’s interactions with voice assistants at home: a bespoke tool for ethical data collection;leveraging world knowledge in implicit hate speech detection;a dataset of sustainable diet arguments on twitter;impacts of low socio-economic status on educational outcomes: a narrative based analysis;enhancing crisis-related tweet classification with entity-masked language modeling and multi-task learning;misinformation detection in the wild: news source classification as a proxy for non-article texts;modelling persuasion through misuse of rhetorical appeals;breaking through inequality of information acquisition among social classes: a modest effort on measuring fun;and identifying condescending language: a tale of two distinct phenomena?.

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Team Cadence at MEDIQA-Chat 2023: Generating, augmenting and summarizing clinical dialogue with large language models 5

Team Cadence at MEDIQA-Chat 2023: Generating, augmenting and...

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5th workshop on Clinical natural language processing, ClinicalNLP 2023. held at ACL 2023

作者： Sharma, Ashwyn Feldman, David I. Jain, Aneesh Cadence Solutions United States Massachusetts General Hospital Harvard University United States Virginia Polytechnic Institute and State University United States

ISBN: (纸本)9781959429883

This paper describes Team Cadence's winning submission to Task C of the MEDIQA-Chat 2023 shared tasks. We also present the set of methods, including a novel N-pass strategy to summarize a mix of clinical dialogue and an incomplete summarized note, used to complete Task A and Task B, ranking highly on the leaderboard amongst stable and reproducible code submissions. The shared tasks invited participants to summarize, classify and generate patient-doctor conversations. Considering the small volume of training data available, we took a data-augmentation-first approach to the three tasks by focusing on the dialogue generation task, i.e., Task C. It proved effective in improving our models' performance on Task A and Task B. We also found the BART architecture to be highly versatile, as it formed the base for all our submissions. Finally, based on the results shared by the organizers, we note that Team Cadence was the only team to submit stable and reproducible runs to all three tasks. © 2023 Association for Computational Linguistics.

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CS/NLP at SemEval-2022 Task 4: Effective Data Augmentation methods for Patronizing language Detection and Multi-label Classification with RoBERTa and GPT3 16

CS/NLP at SemEval-2022 Task 4: Effective Data Augmentation M...

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16th International workshops on Semantic Evaluation (SemEval)

作者： Saeedi, Daniel Saeedi, Sirwe Panahi, Aliakbar Fong, Alvis C. M. Univ Tehran Tehran Iran Western Michigan Univ Kalamazoo MI USA C3 AI Redwood City CA USA

ISBN: (纸本)9781955917803

This paper presents a combination of data augmentation methods to boost the performance of state-of-the-art transformer-based language models for Patronizing and Condescending language (PCL) detection and multi-label PCL classification tasks. These tasks are inherently different from sentiment analysis because positive/negative hidden attitudes in the context will not necessarily be considered positive/negative for PCL tasks. The oblation study observes that the imbalance degree of PCL dataset is in the extreme range. This paper presents a modified version of sentence paraphrasing deep learning model (PEGASUS) to tackle the limitation of maximum sequence length. The proposed algorithm has no specific maximum input length to paraphrase sequences. Our augmented underrepresented class of annotated data achieved competitive results among top-16 SemEval2022 participants. This paper's approaches rely on fine-tuning pretrained RoBERTa and GPT3 models such as Davinci and Curie engines with extra-enriched PCL dataset. Furthermore, we discuss Few-Shot learning technique to overcome the limitation of low-resource NLP problems.

关键词： Curie Data Augmentation Davinci Engines GPT-3 natural language processing RoBERTa Transformers

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