pwc-archive/papers-with-abstracts huggingface dataset:
Content Rating Classification for Fan Fiction
Authors: Yu Qiao, James Pope
Date: 2022-12-23 00:00:00
Tasks: Binary Classification, Classification

Content ratings can enable audiences to determine the suitability of various media products. With the recent advent of fan fiction, the critical issue of fan fiction content ratings has emerged. Whether fan fiction content ratings are done voluntarily or required by regulation, there is the need to automate the content rating classification. The problem is to take fan fiction text and determine the appropriate content rating. Methods for other domains, such as online books, have been attempted though none have been applied to fan fiction. We propose natural language processing techniques, including traditional and deep learning methods, to automatically determine the content rating. We show that these methods produce poor accuracy results for multi-classification. We then demonstrate that treating the problem as a binary classification problem produces better accuracy. Finally, we believe and provide some evidence that the current approach of self-annotating has led to incorrect labels limiting classification results.
Generalizable Natural Language Processing Framework for Migraine Reporting from Social Media
Authors: Yuting Guo, Swati Rajwal, Sahithi Lakamana, Chia-Chun Chiang, Paul C. Menell, Adnan H. Shahid, Yi-Chieh Chen, Nikita Chhabra, Wan-Ju Chao, Chieh-Ju Chao, Todd J. Schwedt, Imon Banerjee, Abeed Sarker
Date: 2022-12-23 00:00:00
Tasks: Management, text-classification, Text Classification

Migraine is a high-prevalence and disabling neurological disorder. However, information migraine management in real-world settings could be limited to traditional health information sources. In this paper, we (i) verify that there is substantial migraine-related chatter available on social media (Twitter and Reddit), self-reported by migraine sufferers; (ii) develop a platform-independent text classification system for automatically detecting self-reported migraine-related posts, and (iii) conduct analyses of the self-reported posts to assess the utility of social media for studying this problem. We manually annotated 5750 Twitter posts and 302 Reddit posts. Our system achieved an F1 score of 0.90 on Twitter and 0.93 on Reddit. Analysis of information posted by our 'migraine cohort' revealed the presence of a plethora of relevant information about migraine therapies and patient sentiments associated with them. Our study forms the foundation for conducting an in-depth analysis of migraine-related information using social media data.
Discovering Customer-Service Dialog System with Semi-Supervised Learning and Coarse-to-Fine Intent Detection
Authors: Zhitong Yang, Xing Ma, Anqi Liu, Zheyu Zhang
Date: 2022-12-23 00:00:00
Tasks: Intent Detection

Task-oriented dialog(TOD) aims to assist users in achieving specific goals through multi-turn conversation. Recently, good results have been obtained based on large pre-trained models. However, the labeled-data scarcity hinders the efficient development of TOD systems at scale. In this work, we constructed a weakly supervised dataset based on a teacher/student paradigm that leverages a large collection of unlabelled dialogues. Furthermore, we built a modular dialogue system and integrated coarse-to-fine grained classification for user intent detection. Experiments show that our method can reach the dialog goal with a higher success rate and generate more coherent responses.
CinPatent: Datasets for Patent Classification
Authors: Minh-Tien Nguyen, Nhung Bui, Manh Tran-Tien, Linh Le, Huy-The Vu
Date: 2022-12-23 00:00:00
Tasks: Classification, Multi Label Text Classification, Multi-Label Text Classification, Patent classification, Question Generation, Question-Generation, Sentence, text-classification, Text Classification, Text Generation

Patent classification is the task that assigns each input patent into several codes (classes). Due to its high demand, several datasets and methods have been introduced. However, the lack of both systematic performance comparison of baselines and access to some datasets creates a gap for the task. To fill the gap, we introduce two new datasets in English and Japanese collected by using CPC codes. The English dataset includes 45,131 patent documents with 425 labels and the Japanese dataset contains 54,657 documents with 523 labels. To facilitate the next studies, we compare the performance of strong multi-label text classification methods on the two datasets. Experimental results show that AttentionXML is consistently better than other strong baselines. The ablation study is also conducted in two aspects: the contribution of different parts (title, abstract, description, and claims) of a patent and the behavior of baselines in terms of performance with different training data segmentation. We release the two new datasets with the code of the baselines.
Dubbing in Practice: A Large Scale Study of Human Localization With Insights for Automatic Dubbing
Authors: William Brannon, Yogesh Virkar, Brian Thompson
Date: 2022-12-23 00:00:00
Tasks: Translation

We investigate how humans perform the task of dubbing video content from one language into another, leveraging a novel corpus of 319.57 hours of video from 54 professionally produced titles. This is the first such large-scale study we are aware of. The results challenge a number of assumptions commonly made in both qualitative literature on human dubbing and machine-learning literature on automatic dubbing, arguing for the importance of vocal naturalness and translation quality over commonly emphasized isometric (character length) and lip-sync constraints, and for a more qualified view of the importance of isochronic (timing) constraints. We also find substantial influence of the source-side audio on human dubs through channels other than the words of the translation, pointing to the need for research on ways to preserve speech characteristics, as well as semantic transfer such as emphasis/emotion, in automatic dubbing systems.
Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times?
Authors: Byung-Doh Oh, William Schuler
Date: 2022-12-23 00:00:00

This work presents a detailed linguistic analysis into why larger Transformer-based pre-trained language models with more parameters and lower perplexity nonetheless yield surprisal estimates that are less predictive of human reading times. First, regression analyses show a strictly monotonic, positive log-linear relationship between perplexity and fit to reading times for the more recently released five GPT-Neo variants and eight OPT variants on two separate datasets, replicating earlier results limited to just GPT-2 (Oh et al., 2022). Subsequently, analysis of residual errors reveals a systematic deviation of the larger variants, such as underpredicting reading times of named entities and making compensatory overpredictions for reading times of function words such as modals and conjunctions. These results suggest that the propensity of larger Transformer-based models to 'memorize' sequences during training makes their surprisal estimates diverge from humanlike expectations, which warrants caution in using pre-trained language models to study human language processing.
Pushing the performances of ASR models on English and Spanish accents
Authors: Pooja Chitkara, Morgane Riviere, Jade Copet, Frank Zhang, Yatharth Saraf
Date: 2022-12-22 00:00:00
Tasks: Speech-to-Text

Speech to text models tend to be trained and evaluated against a single target accent. This is especially true for English for which native speakers from the United States became the main benchmark. In this work, we are going to show how two simple methods: pre-trained embeddings and auxiliary classification losses can improve the performance of ASR systems. We are looking for upgrades as universal as possible and therefore we will explore their impact on several models architectures and several languages.
OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization
Authors: Srinivasan Iyer, Xi Victoria Lin, Ramakanth Pasunuru, Todor Mihaylov, Daniel Simig, Ping Yu, Kurt Shuster, Tianlu Wang, Qing Liu, Punit Singh Koura, Xian Li, Brian O'Horo, Gabriel Pereyra, Jeff Wang, Christopher Dewan, Asli Celikyilmaz, Luke Zettlemoyer, Ves Stoyanov
Date: 2022-12-22 00:00:00
Tasks: Language Modeling, Language Modelling, Meta-Learning, Natural Language Inference, Question Answering

Recent work has shown that fine-tuning large pre-trained language models on a collection of tasks described via instructions, a.k.a. instruction-tuning, improves their zero and few-shot generalization to unseen tasks. However, there is a limited understanding of the performance trade-offs of different decisions made during the instruction-tuning process. These decisions include the scale and diversity of the instruction-tuning benchmark, different task sampling strategies, fine-tuning with and without demonstrations, training using specialized datasets for reasoning and dialogue, and finally, the fine-tuning objectives themselves. In this paper, we characterize the effect of instruction-tuning decisions on downstream task performance when scaling both model and benchmark sizes. To this end, we create OPT-IML Bench: a large benchmark for Instruction Meta-Learning (IML) of 2000 NLP tasks consolidated into task categories from 8 existing benchmarks, and prepare an evaluation framework to measure three types of model generalizations: to tasks from fully held-out categories, to held-out tasks from seen categories, and to held-out instances from seen tasks. Through the lens of this framework, we first present insights about instruction-tuning decisions as applied to OPT-30B and further exploit these insights to train OPT-IML 30B and 175B, which are instruction-tuned versions of OPT. OPT-IML demonstrates all three generalization abilities at both scales on four different evaluation benchmarks with diverse tasks and input formats -- PromptSource, FLAN, Super-NaturalInstructions, and UnifiedSKG. Not only does it significantly outperform OPT on all benchmarks but is also highly competitive with existing models fine-tuned on each specific benchmark. We release OPT-IML at both scales, together with the OPT-IML Bench evaluation framework.
Multi-Lingual DALL-E Storytime
Authors: Noga Mudrik, Adam S. Charles
Date: 2022-12-22 00:00:00

While recent advancements in artificial intelligence (AI) language models demonstrate cutting-edge performance when working with English texts, equivalent models do not exist in other languages or do not reach the same performance level. This undesired effect of AI advancements increases the gap between access to new technology from different populations across the world. This unsought bias mainly discriminates against individuals whose English skills are less developed, e.g., non-English speakers children. Following significant advancements in AI research in recent years, OpenAI has recently presented DALL-E: a powerful tool for creating images based on English text prompts. While DALL-E is a promising tool for many applications, its decreased performance when given input in a different language, limits its audience and deepens the gap between populations. An additional limitation of the current DALL-E model is that it only allows for the creation of a few images in response to a given input prompt, rather than a series of consecutive coherent frames that tell a story or describe a process that changes over time. Here, we present an easy-to-use automatic DALL-E storytelling framework that leverages the existing DALL-E model to enable fast and coherent visualizations of non-English songs and stories, pushing the limit of the one-step-at-a-time option DALL-E currently offers. We show that our framework is able to effectively visualize stories from non-English texts and portray the changes in the plot over time. It is also able to create a narrative and maintain interpretable changes in the description across frames. Additionally, our framework offers users the ability to specify constraints on the story elements, such as a specific location or context, and to maintain a consistent style throughout the visualization.
Text classification in shipping industry using unsupervised models and Transformer based supervised models
Authors: Ying Xie, Dongping Song
Date: 2022-12-21 00:00:00
Tasks: Classification, Self-Learning, text-classification, Text Classification, Unsupervised Text Classification, Word Embeddings

Obtaining labelled data in a particular context could be expensive and time consuming. Although different algorithms, including unsupervised learning, semi-supervised learning, self-learning have been adopted, the performance of text classification varies with context. Given the lack of labelled dataset, we proposed a novel and simple unsupervised text classification model to classify cargo content in international shipping industry using the Standard International Trade Classification (SITC) codes. Our method stems from representing words using pretrained Glove Word Embeddings and finding the most likely label using Cosine Similarity. To compare unsupervised text classification model with supervised classification, we also applied several Transformer models to classify cargo content. Due to lack of training data, the SITC numerical codes and the corresponding textual descriptions were used as training data. A small number of manually labelled cargo content data was used to evaluate the classification performances of the unsupervised classification and the Transformer based supervised classification. The comparison reveals that unsupervised classification significantly outperforms Transformer based supervised classification even after increasing the size of the training dataset by 30%. Lacking training data is a key bottleneck that prohibits deep learning models (such as Transformers) from successful practical applications. Unsupervised classification can provide an alternative efficient and effective method to classify text when there is scarce training data.
Capacity Studies for a Differential Growing Neural Gas
Authors: P. Levi, P. Gelhausen, G. Peters
Date: 2022-12-23 00:00:00
Tasks: valid

In 2019 Kerdels and Peters proposed a grid cell model (GCM) based on a Differential Growing Neural Gas (DGNG) network architecture as a computationally efficient way to model an Autoassociative Memory Cell (AMC) \cite{Kerdels_Peters_2019}. An important feature of the DGNG architecture with respect to possible applications in the field of computational neuroscience is its \textit{capacity} refering to its capability to process and uniquely distinguish input signals and therefore obtain a valid representation of the input space. This study evaluates the capacity of a two layered DGNG grid cell model on the Fashion-MNIST dataset. The focus on the study lies on the variation of layer sizes to improve the understanding of capacity properties in relation to network parameters as well as its scaling properties. Additionally, parameter discussions and a plausability check with a pixel/segment variation method are provided. It is concluded, that the DGNG model is able to obtain a meaningful and plausible representation of the input space and to cope with the complexity of the Fashion-MNIST dataset even at moderate layer sizes.
hxtorch.snn: Machine-learning-inspired Spiking Neural Network Modeling on BrainScaleS-2
Authors: Philipp Spilger, Elias Arnold, Luca Blessing, Christian Mauch, Christian Pehle, Eric Müller, Johannes Schemmel
Date: 2022-12-23 00:00:00

Neuromorphic systems require user-friendly software to support the design and optimization of experiments. In this work, we address this need by presenting our development of a machine learning-based modeling framework for the BrainScaleS-2 neuromorphic system. This work represents an improvement over previous efforts, which either focused on the matrix-multiplication mode of BrainScaleS-2 or lacked full automation. Our framework, called hxtorch.snn, enables the hardware-in-the-loop training of spiking neural networks within PyTorch, including support for auto differentiation in a fully-automated hardware experiment workflow. In addition, hxtorch.snn facilitates seamless transitions between emulating on hardware and simulating in software. We demonstrate the capabilities of hxtorch.snn on a classification task using the Yin-Yang dataset employing a gradient-based approach with surrogate gradients and densely sampled membrane observations from the BrainScaleS-2 hardware system.
Recommending on graphs: a comprehensive review from a data perspective
Authors: Lemei Zhang, Peng Liu, Jon Atle Gulla
Date: 2022-12-23 00:00:00
Tasks: Diversity, Fairness, Graph Learning, Network Embedding, Recommendation Systems

Recent advances in graph-based learning approaches have demonstrated their effectiveness in modelling users' preferences and items' characteristics for Recommender Systems (RSS). Most of the data in RSS can be organized into graphs where various objects (e.g., users, items, and attributes) are explicitly or implicitly connected and influence each other via various relations. Such a graph-based organization brings benefits to exploiting potential properties in graph learning (e.g., random walk and network embedding) techniques to enrich the representations of the user and item nodes, which is an essential factor for successful recommendations. In this paper, we provide a comprehensive survey of Graph Learning-based Recommender Systems (GLRSs). Specifically, we start from a data-driven perspective to systematically categorize various graphs in GLRSs and analyze their characteristics. Then, we discuss the state-of-the-art frameworks with a focus on the graph learning module and how they address practical recommendation challenges such as scalability, fairness, diversity, explainability and so on. Finally, we share some potential research directions in this rapidly growing area.
Adaptive Risk-Aware Bidding with Budget Constraint in Display Advertising
Authors: Zhimeng Jiang, Kaixiong Zhou, Mi Zhang, Rui Chen, Xia Hu, Soo-Hyun Choi
Date: 2022-12-06 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Real-time bidding (RTB) has become a major paradigm of display advertising. Each ad impression generated from a user visit is auctioned in real time, where demand-side platform (DSP) automatically provides bid price usually relying on the ad impression value estimation and the optimal bid price determination. However, the current bid strategy overlooks large randomness of the user behaviors (e.g., click) and the cost uncertainty caused by the auction competition. In this work, we explicitly factor in the uncertainty of estimated ad impression values and model the risk preference of a DSP under a specific state and market environment via a sequential decision process. Specifically, we propose a novel adaptive risk-aware bidding algorithm with budget constraint via reinforcement learning, which is the first to simultaneously consider estimation uncertainty and the dynamic risk tendency of a DSP. We theoretically unveil the intrinsic relation between the uncertainty and the risk tendency based on value at risk (VaR). Consequently, we propose two instantiations to model risk tendency, including an expert knowledge-based formulation embracing three essential properties and an adaptive learning method based on self-supervised reinforcement learning. We conduct extensive experiments on public datasets and show that the proposed framework outperforms state-of-the-art methods in practical settings.
Using support vector regression and K-nearest neighbors for short-term traffic flow prediction based on maximal information coefficient
Authors: Guancen Lin, Aijing Lin ⇑, Danlei Gu
Date: 2022-06-23 00:00:00
Proceeding: 27 June 2022 6
Tasks: Management, Prediction, Traffic Prediction

The prediction of short-term traffic flow is critical for improving service levels for drivers
and passengers as well as enhancing the efficiency of traffic management in the urban
transportation system. For transportation departments, the issue remains of how to effi-
ciently utilize the spatial and temporal information of traffic data for better prediction per-
formance. As a means of improving traffic prediction accuracy, this paper proposes a
method for screening spatial time-delayed traffic series based on the maximal information
coefficient. The selected time-delayed traffic series are transformed into traffic state vec-
tors, from which traffic flow is predicted by adopting the combination of support vector
regression method and k-nearest neighbors method. We employ the proposed framework
for real-world traffic flow prediction. Root Mean Squared Error (RMSE) and Mean Absolute
Percent Error (MAPE) validate the superior performance of the proposed model compared
to traditional methods. This new approach reduces the RMSE by 23.448% and the MAPE by
14.726% of the predicted results.
Adaptive Radial Projection on Fourier Magnitude Spectrum for Document Image Skew Estimation
Authors: Luan Pham; Phu Hao Hoang; Xuan Toan Mai; Tuan Anh Tran
Date: 2022-10-18 00:00:00
Proceeding: IEEE International Conference on Image Processing (ICIP) 2022 10
Tasks: Document Image Skew Estimation

Skew estimation is one of the vital tasks in document processing systems, especially for scanned document images, because its performance impacts subsequent steps directly. Over the years, an enormous number of researches focus on this challenging problem in the rise of digitization age. In this research, we first propose a novel skew estimation method that extracts the dominant skew angle of the given document image by applying an Adaptive Radial Projection on the 2D Discrete Fourier Magnitude spectrum. Second, we introduce a high quality skew estimation dataset DISE-2021 to assess the performance of different estimators. Finally, we provide comprehensive analyses that focus on multiple improvement aspects of Fourier-based methods. Our results show that the proposed method is robust, reliable, and outperforms all compared methods. Data and code are available at https://github.com/phamquiluan/jdeskew.
The Effects of Just-in-time Delivery on Social Engagement: A Cluster Analysis
Authors: Moisés Ramírez, Raziel Ruíz, Nathan Klarer
Date: 2022-12-23 00:00:00
Tasks: Clustering, Marketing

Fooji Inc. is a social media engagement platform that has created a proprietary "Just-in-time" delivery network to provide prizes to social media marketing campaign participants in real-time. In this paper, we prove the efficacy of the "Just-in-time" delivery network through a cluster analysis that extracts and presents the underlying drivers of campaign engagement. We utilize a machine learning methodology with a principal component analysis to organize Fooji campaigns across these principal components. The arrangement of data across the principal component space allows us to expose underlying trends using a $K$-means clustering technique. The most important of these trends is the demonstration of how the "Just-in-time" delivery network improves social media engagement.