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arXiv 2024年

作者： Senger, Elena Campbell, Yuri van der Goot, Rob Plank, Barbara MaiNLP Center for Information and Language Processing LMU Munich Germany Fraunhofer Center for International Management and Knowledge Economy IMW Germany Department of Computer Science IT University of Copenhagen Denmark

Accurate career path prediction can support many stakeholders, like job seekers, recruiters, HR, and project managers. However, publicly available data and tools for career path prediction are scarce. In this work, we introduce KARRIEREWEGE, a comprehensive, publicly available dataset containing over 500k career paths, significantly surpassing the size of previously available datasets. We link the dataset to the ESCO taxonomy to offer a valuable resource for predicting career trajectories. To tackle the problem of free-text inputs typically found in resumes, we enhance it by synthesizing job titles and descriptions resulting in KARRIEREWEGE+. This allows for accurate predictions from unstructured data, closely aligning with real-world application challenges. We benchmark existing state-of-the-art (SOTA) models on our dataset and a prior benchmark and observe improved performance and robustness, particularly for free-text use cases, due to the synthesized data. © 2024, CC BY.

关键词： Prediction models

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A Novel Multimodal Sentiment Analysis Model Based on Gated Fusion and Multi-Task Learning

A Novel Multimodal Sentiment Analysis Model Based on Gated F...

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International Conference on Acoustics, speech, and Signal processing (ICASSP)

作者： Xin Sun Xiangyu Ren Xiaohao Xie School of Computer Science and Technology Beijing Institute of Technology China Beijing Engineering Applications Research Center on High Volume Language Information Processing and Cloud Computing

Sentiment analysis is an important research area in Natural language processing (NLP). With the explosion of multimodal data, Multimodal Sentiment Analysis (MSA) attracts more and more attention in recent years. How to Effectively harnessing the interplay between diverse modalities is paramount to achieving comprehensive fusion of MSA. However, current research predominantly emphasizes modality interaction, while overlooking unimodal information, thus neglecting the inherent disparities between modalities. To address these issues, we propose a novel model for multimodal sentiment analysis based on gated fusion and multi-task learning. The model adopts multi-task learning to concurrently address both multimodal and unimodal sentiment analysis tasks. Specifically, for the multimodal task, we leverage cross-modal Transformers with gating mechanisms to facilitate modality fusion. Subsequently, the fused representations are harnessed to generate sentiment labels for the unimodal tasks. Experiments on the CMU-MOSI and CMU-MOSEI datasets demonstrate that our model outperforms the existing methods and achieves the state-of-the art performance.

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Wiktionary Normalization of Translations and Morphological Information 28

Wiktionary Normalization of Translations and Morphological I...

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28th International Conference on Computational Linguistics, COLING 2020

作者： Wu, Winston Yarowsky, David Department of Computer Science Center for Language and Speech Processing Johns Hopkins University United States

ISBN: (纸本)9781952148279

We extend the Yawipa Wiktionary Parser (Wu and Yarowsky, 2020) to extract and normalize translations from etymology glosses, and morphological form-of relations, resulting in 300K unique translations and over 4 million instances of 168 annotated morphological relations. We propose a method to identify typos in translation annotations. Using the extracted morphological data, we develop multilingual neural models for predicting three types of word formation—clipping, contraction, and eye dialect—and improve upon a standard attention baseline by using copy attention. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Translation (languages)

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VE-KWS: Visual Modality Enhanced End-to-End Keyword Spotting

VE-KWS: Visual Modality Enhanced End-to-End Keyword Spotting

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International Conference on Acoustics, speech, and Signal processing (ICASSP)

作者： Ao Zhang He Wang Pengcheng Guo Yihui Fu Lei Xie Yingying Gao Shilei Zhang Junlan Feng Audio Speech and Language Processing Group (ASLP@NPU) School of Computer Science Northwestern Polytechnical University Xi’an China China Mobile Research Institute Beijing China

The performance of the keyword spotting (KWS) system based on audio modality, commonly measured in false alarms and false rejects, degrades significantly under the far field and noisy conditions. Therefore, audio-visual keyword spotting, which leverages complementary relationships over multiple modalities, has recently gained much attention. However, current studies mainly focus on combining the exclusively learned representations of different modalities, instead of exploring the modal relationships during each respective modeling. In this paper, we propose a novel visual modality enhanced end-to-end KWS framework (VE-KWS), which fuses audio and visual modalities from two aspects. The first one is utilizing the speaker location information obtained from the lip region in videos to assist the training of multi-channel audio beamformer. By involving the beamformer as an audio enhancement module, the acoustic distortions, caused by the far field or noisy environments, could be significantly suppressed. The other one is conducting cross-attention between different modalities to capture the inter-modal relationships and help the representation learning of each modality. Experiments on the MSIP challenge corpus show that our proposed model achieves a 2.79% false rejection rate and a 2.95% false alarm rate on the Eval set, resulting in a new SOTA performance compared with the top-ranking systems in the ICASSP2022 MISP challenge.

关键词： Training Representation learning Visualization Fuses Lips Noise reduction Real-time systems

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Establishing Trustworthiness: Rethinking Tasks and Model Evaluation

arXiv

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arXiv 2023年

作者： Litschko, Robert Müller-Eberstein, Max van der Goot, Rob Weber, Leon Plank, Barbara MaiNLP Center for Information and Language Processing LMU Munich Germany Department of Computer Science IT University of Copenhagen Denmark Munich Germany

language understanding is a multi-faceted cognitive capability, which the Natural language processing (NLP) community has striven to model computationally for decades. Traditionally, facets of linguistic intelligence have been compartmentalized into tasks with specialized model architectures and corresponding evaluation protocols. With the advent of large language models (LLMs) the community has witnessed a dramatic shift towards general purpose, task-agnostic approaches powered by generative models. As a consequence, the traditional compartmentalized notion of language tasks is breaking down, followed by an increasing challenge for evaluation and analysis. At the same time, LLMs are being deployed in more real-world scenarios, including previously unforeseen zero-shot setups, increasing the need for trustworthy and reliable systems. Therefore, we argue that it is time to rethink what constitutes tasks and model evaluation in NLP, and pursue a more holistic view on language, placing trustworthiness at the center. Towards this goal, we review existing compartmentalized approaches for understanding the origins of a model’s functional capacity, and provide recommendations for more multifaceted evaluation protocols. © 2023, CC BY.

关键词： Natural language processing systems

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Donkii: Can Annotation Error Detection Methods Find Errors in Instruction-Tuning Datasets?

arXiv

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arXiv 2023年

作者： Weber-Genzel, Leon Litschko, Robert Artemova, Ekaterina Plank, Barbara MaiNLP Center for Information and Language Processing LMU Munich Germany Munich Germany Department of Computer Science IT University of Copenhagen Denmark

Instruction tuning has become an integral part of training pipelines for Large language Models (LLMs) and has been shown to yield strong performance gains. In an orthogonal line of research, Annotation Error Detection (AED) has emerged as a tool for detecting quality problems in gold standard labels. So far, however, the application of AED methods has been limited to classification tasks. It is an open question how well AED methods generalize to language generation settings, which are becoming more widespread via LLMs. In this paper, we present a first and novel benchmark for AED on instruction tuning data: DONKII. It comprises three instruction-tuning datasets enriched with error annotations by experts and semi-automatic methods. We also provide a novel taxonomy of error types for instruction-tuning data. We find that all three datasets contain clear errors, which sometimes propagate directly into instruction-tuned LLMs. We propose four AED baselines for the generative setting and evaluate them extensively on the newly introduced dataset. Our results show that the choice of the right AED method and model size is indeed crucial and derive practical recommendations for how to use AED methods to clean instruction-tuning data. © 2023, CC BY.

关键词： Error detection

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Can Automated speech Recognition Errors Provide Valuable Clues for Alzheimer’s Disease Detection?

Can Automated Speech Recognition Errors Provide Valuable Clu...

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International Conference on Acoustics, speech, and Signal processing (ICASSP)

作者： Yin-Long Liu Rui Feng Ye-Xin Lu Jia-Xin Chen Yang Ai Jia-Hong Yuan Zhen-Hua Ling National Engineering Research Center of Speech and Language Information Processing University of Science and Technology of China Hefei P. R. China Interdisciplinary Research Center for Linguistic Sciences University of Science and Technology of China Hefei P. R. China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent advances in automatic speech recognition (ASR) technology have boosted the viability of fully automated Alzheimer’s disease (AD) detection via ASR transcripts. However, there is a lack of understanding of how ASR errors affect the performance of AD detection. This paper addresses that gap. First, we fine-tune 18 ASR models on three datasets from DementiaBank, generating 36 ASR transcripts on the ADReSS dataset (18 from original and 18 from fine-tuned ASR models). We then employ two AD detection methods using either ASR or manual transcripts: fine-tuning four large language models (LLMs) and fusing LLMs with pre-trained language models (PLMs). The results show that certain ASR transcripts outperform manual transcripts, suggesting that ASR errors provide valuable clues for AD detection. Finally, we conduct an interpretability study, including linguistic and SHapley Additive exPlanations (SHAP) analyses. This study reveals that greater word distribution differences between AD and healthy control (HC) groups in ASR transcripts may be linked to these valuable clues. This paper highlights the potential of ASR as a powerful tool for developing fully automated AD detection systems.

关键词： Systematics Additives Large language models Manuals Signal processing Linguistics Acoustics Alzheimer's disease speech processing Automatic speech recognition

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Transfer learning for automated responses to the BDI questionnaire

Transfer learning for automated responses to the BDI questio...

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2021 Working Notes of CLEF - Conference and Labs of the Evaluation Forum, CLEF-WN 2021

作者： Spartalis, Christoforos Drosatos, George Arampatzis, Avi Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi67100 Greece Institute for Language and Speech Processing Athena Research Center Xanthi67100 Greece

This paper describes the participation of the DUTH-ATHENA team of Democritus University of Thrace and Athena Research center in the eRisk 2021 task, which focuses on measuring the level of depression based on Reddit users' posts. We address this task using both feature-based and fine-tuning strategies for applying BERT-based representations. In the feature-based approaches, we examine the possibilities of a SBERT model based on RoBERTa, pre-trained on Natural language Inference (NLI) data and fine-tuned on STSb dataset to leverage transfer learning to depression-level estimation, and we achieve promising results. One of our runs ranks first in Average Hit Rate (AHR), while the others rank among the best four in the other evaluation metrics. Also, for the fine-tuning approach, we propose two predictive models that are built upon RoBERTa, which provide directions for future optimizations. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Social networking (online)

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Keyword-based Natural language Premise Selection for an Automatic Mathematical Statement Proving 16

Keyword-based Natural Language Premise Selection for an Auto...

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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： Dastgheib, Doratossadat Asgari, Ehsaneddin Language Processing and Digital Humanities Lab Tehran Iran Department of Computer and Data Science Shahid Beheshti University Tehran Iran NLP Expert Center Data:Lab Volkswagen AG Munich Germany

Extraction of supportive premises for a mathematical problem can contribute to profound success in improving automatic reasoning systems. One bottleneck in automated theorem proving is the lack of a proper semantic information retrieval system for mathematical texts. In this paper, we show the effect of keyword extraction in the natural language premise selection (NLPS) shared task proposed in TextGraph-16 that seeks to select the most relevant sentences supporting a given mathematical statement. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Extraction

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Deep CLAS: Deep Contextual Listen, Attend and Spell

arXiv

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arXiv 2024年

作者： Wang, Mengzhi Xiong, Shifu Wan, Genshun Chen, Hang Gao, Jianqing Dai, Lirong iFLYTEK Research iFLYTEK Co. Ltd. Hefei230088 China National Engineering Research Center of Speech and Language Information Processing University of Science and Technology of China Hefei230027 China

Contextual-LAS (CLAS) has been shown effective in improving Automatic speech Recognition (ASR) of rare words. It relies on phrase-level contextual modeling and attention-based relevance scoring without explicit contextual constraint which lead to insufficient use of contextual information. In this work, we propose deep CLAS to deeply utilize contextual information. We introduce bias loss forcing model to focus on contextual information. The query of bias attention is also enriched to improve the accuracy of the bias attention score. To get fine-grained contextual information, we replace phrase-level encoding with character-level encoding and encode contextual information with conformer. Furthermore, the bias attention score is directly utilized to correct the model’s output probability distribution. Additionally, a prefix tree is employed to prevent interference from irrelevant information. Experiments using the public AISHELL-1. Compared to CLAS baselines, deep CLAS obtains a 65.78% relative recall and a 53.49% relative F1-score increase in the named entity recognition scene. © 2024, CC BY.

关键词： Encoding (symbols)

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