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Bilingual Lexicon Induction for Low-Resource languages using Graph Matching via Optimal Transport

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arXiv 2022年

作者： Marchisio, Kelly Saad-Eldin, Ali Duh, Kevin Priebe, Carey Koehn, Philipp Department of Computer Science United States Department of Applied Mathematics and Statistics United States Department of Biomedical Engineering United States Human Language Technology Center of Excellence Johns Hopkins University United States

Bilingual lexicons form a critical component of various natural language processing applications, including unsupervised and semisupervised machine translation and crosslingual information retrieval. We improve bilingual lexicon induction performance across 40 language pairs with a graph-matching method based on optimal transport. The method is especially strong with low amounts of supervision. Copyright © 2022, The Authors. All rights reserved.

关键词： Natural language processing systems

Adapting Coreference Resolution Models through Active Learning

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arXiv 2021年

作者： Yuan, Michelle Xia, Patrick May, Chandler Van Durme, Benjamin Boyd-Graber, Jordan Human Language Technology Center of Excellence United States University of Maryland United States Johns Hopkins University United States

Neural coreference resolution models trained on one dataset may not transfer to new, low-resource domains. Active learning mitigates this problem by sampling a small subset of data for annotators to label. While active learning is well-defined for classification tasks, its application to coreference resolution is neither well-defined nor fully understood. This paper explores how to actively label coreference, examining sources of model uncertainty and document reading costs. We compare uncertainty sampling strategies and their advantages through thorough error analysis. In both synthetic and human experiments, labeling spans within the same document is more effective than annotating spans across documents. The findings contribute to a more realistic development of coreference resolution models. © 2021, CC BY-SA.

关键词： Uncertainty analysis

Beyond Click to Cognition: Effective Interventions for Promoting Examination of False Beliefs in Misinformation 25

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Beyond Click to Cognition: Effective Interventions for Promo...

Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Yuko Tanaka Hiromi Arai Miwa Inuzuka Yoichi Takahashi Minao Kukita Ryuta Iseki Kentaro Inui Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan Center for Advanced Intelligence Project RIKEN Tokyo Japan Department of Education Tokyo Gakugei University Tokyo Japan Graduate School of Informatics Nagoya University Nagoya Japan Department of Human Sciences Taisho University Tokyo Japan Natural Language Processing Department MBZUAI Abu Dhabi United Arab Emirates

ISBN: (纸本)9798400713941

In the digital information ecosystem, clicks serve as a crucial gateway to fact-checking, yet the essential challenge extends beyond this to fostering cognitive shifts that update entrenched false beliefs. This study investigates effective interventions aimed at encouraging users vulnerable to misinformation, particularly those who tend to avoid incongruent facts, to examine their false beliefs. We conducted an online experiment with 627 participants, comparing metacognitive and ranking interventions. Both interventions successfully improved click behavior, with the metacognitive intervention increasing belief-examining clicks by 14 percentage points and the ranking intervention by 33 percentage points. However, only the metacognitive intervention significantly promoted users examination of misinformation. This finding underscores the importance of interventions that go beyond merely influencing easily measurable clicks to facilitating thoughtful engagement with factchecking content. We discuss implications for designing strategies to enhance online fact-checking engagement and mitigate misinformation's societal impact. © 2025 Copyright held by the owner/author(s).

关键词： Misinformation

Training noisy single-channel speech separation with noisy oracle sources: A large gap and a small step

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arXiv 2020年

作者： Maciejewski, Matthew Shi, Jing Watanabe, Shinji Khudanpur, Sanjeev Center for Language and Speech Processing The Johns Hopkins University United States Human Language Technology Center of Excellence The Johns Hopkins University United States Institute of Automation Chinese Academy of Sciences China

As the performance of single-channel speech separation systems has improved, there has been a desire to move to more challenging conditions than the clean, near-field speech that initial systems were developed on. When training deep learning separation models, a need for ground truth leads to training on synthetic mixtures. As such, training in noisy conditions requires either using noise synthetically added to clean speech, preventing the use of in-domain data for a noisy-condition task, or training using mixtures of noisy speech, requiring the network to additionally separate the noise. We demonstrate the relative inseparability of noise and that this noisy speech paradigm leads to significant degradation of system performance. We also propose an SI-SDR–inspired training objective that tries to exploit the inseparability of noise to implicitly partition the signal and discount noise separation errors, enabling the training of better separation systems with noisy oracle sources. Copyright © 2020, The Authors. All rights reserved.

关键词： speech analysis

Influence of emotional states on incongruity-resolution and nonsense humour processing

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International journal of psychology : Journal international de psychologie 2024年第6期59卷 1091-1100页

作者： Roy Soh Ching-Lin Wu Ping-Hsun Tsai Hsueh-Chih Chen School of Education and Human Sciences Albukhary International University Alor Setar Malaysia. Program of Learning Sciences National Taiwan Normal University Taipei Taiwan. Institute for Research Excellence in Learning Sciences National Taiwan Normal University Taipei Taiwan. Department of Educational Psychology and Counseling National Taiwan Normal University Taipei Taiwan. Chinese Language and Technology Center National Taiwan Normal University Taipei Taiwan.

Humour processing is complex and may be influenced by an individual's emotional state. This study explores the differences in incongruity-resolution and nonsense joke humour processing between three groups with different emotional states (positive, neutral and negative). Each group consisted of 32 participants. The positive and negative emotion groups were asked to elicit positive and negative emotions, respectively, through autobiographical recall. In contrast, the neutral emotion group was asked to answer questions unrelated to emotions. Participants were also asked to rate their emotional valence, arousal and conformity with the provided emotion words. Subsequently, they were directed to rate their humour appreciation, humour comprehension and level of surprise at the jokes. The results indicate that these variables interacted with the level of amusement derived from the two humour types. The positive emotion group perceived nonsense jokes as funnier than the negative emotion group, while the negative emotion group perceived nonsense jokes as less amusing than incongruity-resolution jokes. No significant difference was found between the three emotion groups in terms of humour comprehension and surprise at the jokes. The results deepen the understanding of the correlation between emotional states and processing of different humour types.

关键词： Emotional state Humour appreciation Humour comprehension Incongruity‐resolution humour Nonsense humour

WIKIVIDEO: Article Generation from Multiple Videos

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arXiv 2025年

作者： Martin, Alexander Kriz, Reno Walden, William Gantt Sanders, Kate Recknor, Hannah Yang, Eugene Ferraro, Francis Van Durme, Benjamin Johns Hopkins University United States Human Language Technology Center of Excellence United States University of Maryland Baltimore County United States

We present the challenging task of automatically creating a high-level Wikipedia-style article that aggregates information from multiple diverse videos about real-world events, such as natural disasters or political elections. Videos are intuitive sources for retrieval-augmented generation (RAG), but most contemporary RAG workflows focus heavily on text while existing methods for video-based summarization focus on low-level scene understanding rather than high-level event semantics. To close this gap, we introduce WIKIVIDEO, a benchmark consisting of expert-written articles and densely annotated videos that provide evidence for articles’ claims, facilitating the integration of video into RAG pipelines and enabling the creation of in-depth content that is grounded in multimodal sources. We further propose Collaborative Article Generation (CAG), a novel interactive method for article creation from multiple videos. CAG leverages an iterative interaction between an r1-style reasoning model and a VideoLLM to draw higher-level inferences about the target event than is possible with VideoLLMs alone, which fixate on low-level visual features. We benchmark state-of-the-art VideoLLMs and CAG in both oracle retrieval and RAG settings and find that CAG consistently outperforms alternative methods, while suggesting intriguing avenues for future work. © 2025, CC BY-SA.

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Dover-lap: A method for combining overlap-aware diarization outputs

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arXiv 2020年

作者： Raj, Desh Garcia-Perera, Leibny Paola Huang, Zili Watanabe, Shinji Povey, Daniel Stolcke, Andreas Khudanpur, Sanjeev Center for Language and Speech Processing & Human Language Technlogy Center of Excellence The Johms Hopkins University BaltimoreMD21218 United States Human Language Technology Center of Excellence Johns Hopkins University BaltimoreMD21218 United States Xiaomi Corp. Beijing China Amazon Alexa Speech SunnyvaleCA United States

Several advances have been made recently towards handling overlapping speech for speaker diarization. Since speech and natural language tasks often benefit from ensemble techniques, we propose an algorithm for combining outputs from such diarization systems through majority voting. Our method, DOVER-Lap, is inspired from the recently proposed DOVER algorithm, but is designed to handle overlapping segments in diarization outputs. We also modify the pair-wise incremental label mapping strategy used in DOVER, and propose an approximation algorithm based on weighted k-partite graph matching, which performs this mapping using a global cost tensor. We demonstrate the strength of our method by combining outputs from diverse systems — clustering-based, region proposal networks, and target-speaker voice activity detection — on AMI and LibriCSS datasets, where it consistently outperforms the single best system. Additionally, we show that DOVER-Lap can be used for late fusion in multichannel diarization, and compares favorably with early fusion methods like beamforming. © 2020, CC-BY.

关键词： Approximation algorithms

BISECT: Learning to split and rephrase sentences with bitexts

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arXiv 2021年

作者： Kim, Joongwon Maddela, Mounica Kriz, Reno Xu, Wei Callison-Burch, Chris Department of Computer and Information Science University of Pennsylvania School of Interactive Computing Georgia Institute of Technology Human Language Technology Center of Excellence Johns Hopkins University

An important task in NLP applications such as sentence simplification is the ability to take a long, complex sentence and split it into shorter sentences, rephrasing as necessary. We introduce a novel dataset and a new model for this 'split and rephrase' task. Our BISECT training data consists of 1 million long English sentences paired with shorter, meaning-equivalent English sentences. We obtain these by extracting 1-2 sentence alignments in bilingual parallel corpora and then using machine translation to convert both sides of the corpus into the same language. BISECT contains higher quality training examples than previous Split and Rephrase corpora, with sentence splits that require more significant modifications. We categorize examples in our corpus, and use these categories in a novel model that allows us to target specific regions of the input sentence to be split and edited. Moreover, we show that models trained on BISECT can perform a wider variety of split operations and improve upon previous state-of-the-art approaches in automatic and human evaluations. © 2021, CC BY.

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SPECTRE: Visual speech-Informed Perceptual 3D Facial Expression Reconstruction from Videos

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SPECTRE: Visual Speech-Informed Perceptual 3D Facial Express...

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Panagiotis P. Filntisis George Retsinas Foivos Paraperas-Papantoniou Athanasios Katsamanis Anastasios Roussos Petros Maragos Institute of Robotics Athena Research Center Maroussi Greece School of Electrical & Computer Engineering National Technical University of Athens Greece Imperial College London UK Institute for Language and Speech Processing Athena R.C. Greece Institute of Computer Science (ICS) Foundation for Research & Technology - Hellas (FORTH) Greece College of Engineering Mathematics and Physical Sciences University of Exeter UK

The recent state of the art on monocular 3D face reconstruction from image data has made some impressive advancements, thanks to the advent of Deep Learning. However, it has mostly focused on input coming from a single RGB image, overlooking the following important factors: a) Nowadays, the vast majority of facial image data of interest do not originate from single images but rather from videos, which contain rich dynamic information. b) Furthermore, these videos typically capture individuals in some form of verbal communication (public talks, teleconferences, audiovisual human-computer interactions, interviews, monologues/dialogues in movies, etc). When existing 3D face reconstruction methods are applied in such videos, the artifacts in the reconstruction of the shape and motion of the mouth area are often severe, since they do not match well with the speech *** overcome the aforementioned limitations, we present the first method for visual speech-informed perceptual reconstruction of 3D mouth expressions. We do this by proposing a "lipreading" loss, which guides the fitting process so that the elicited perception from the 3D reconstructed talking head resembles that of the original video footage. We demonstrate that, interestingly, the lipreading loss is better suited for 3D reconstruction of mouth movements compared to traditional landmark losses, and even direct 3D supervision. Furthermore, the devised method does not rely on any text transcriptions or corresponding audio, rendering it ideal for training in unlabeled datasets. We verify the efficiency of our method through objective evaluations on three large-scale datasets, as well as subjective evaluation with two web-based user studies. Project webpage: https://***/spectre/

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