pwc-archive/papers-with-abstracts huggingface dataset:
Towards Making the Most of ChatGPT for Machine Translation
Authors: Keqin Peng, Liang Ding, Qihuang Zhong, Li Shen, Xuebo Liu, Min Zhang, Yuanxin Ouyang, DaCheng Tao
Date: 2023-03-24 00:00:00
Tasks: In-Context Learning, Machine Translation, Translation, Word Translation

ChatGPT shows remarkable capabilities for machine translation (MT). Several prior studies have shown that it achieves comparable results to commercial systems for high-resource languages, but lags behind in complex tasks, e.g., low-resource and distant-language-pairs translation. However, they usually adopt simple prompts which can not fully elicit the capability of ChatGPT. In this paper, we aim to further mine ChatGPT's translation ability by revisiting several aspects: temperature, task information, and domain information, and correspondingly propose an optimal temperature setting and two (simple but effective) prompts: Task-Specific Prompts (TSP) and Domain-Specific Prompts (DSP). We show that: 1) The performance of ChatGPT depends largely on temperature, and a lower temperature usually can achieve better performance; 2) Emphasizing the task information can further improve ChatGPT's performance, particularly in complex MT tasks; 3) Introducing domain information can elicit ChatGPT's generalization ability and improve its performance in the specific domain; 4) ChatGPT tends to generate hallucinations for non-English-centric MT tasks, which can be partially addressed by our proposed prompts but still need to be highlighted for the MT/NLP community. We also explore the effects of advanced in-context learning strategies and find a (negative but interesting) observation: the powerful chain-of-thought prompt leads to word-by-word translation behavior, thus bringing significant translation degradation.
Natural language processing to automatically extract the presence and severity of esophagitis in notes of patients undergoing radiotherapy
Authors: Shan Chen, Marco Guevara, Nicolas Ramirez, Arpi Murray, Jeremy L. Warner, Hugo JWL Aerts, Timothy A. Miller, Guergana K. Savova, Raymond H. Mak, Danielle S. Bitterman
Date: 2023-03-24 00:00:00
Tasks: Task 2

Radiotherapy (RT) toxicities can impair survival and quality-of-life, yet remain under-studied. Real-world evidence holds potential to improve our understanding of toxicities, but toxicity information is often only in clinical notes. We developed natural language processing (NLP) models to identify the presence and severity of esophagitis from notes of patients treated with thoracic RT. We fine-tuned statistical and pre-trained BERT-based models for three esophagitis classification tasks: Task 1) presence of esophagitis, Task 2) severe esophagitis or not, and Task 3) no esophagitis vs. grade 1 vs. grade 2-3. Transferability was tested on 345 notes from patients with esophageal cancer undergoing RT. Fine-tuning PubmedBERT yielded the best performance. The best macro-F1 was 0.92, 0.82, and 0.74 for Task 1, 2, and 3, respectively. Selecting the most informative note sections during fine-tuning improved macro-F1 by over 2% for all tasks. Silver-labeled data improved the macro-F1 by over 3% across all tasks. For the esophageal cancer notes, the best macro-F1 was 0.73, 0.74, and 0.65 for Task 1, 2, and 3, respectively, without additional fine-tuning. To our knowledge, this is the first effort to automatically extract esophagitis toxicity severity according to CTCAE guidelines from clinic notes. The promising performance provides proof-of-concept for NLP-based automated detailed toxicity monitoring in expanded domains.
ReCOGS: How Incidental Details of a Logical Form Overshadow an Evaluation of Semantic Interpretation
Authors: Zhengxuan Wu, Christopher D. Manning, Christopher Potts
Date: 2023-03-24 00:00:00
Tasks: Form, Semantic Parsing

Compositional generalization benchmarks for semantic parsing seek to assess whether models can accurately compute meanings for novel sentences, but operationalize this in terms of logical form (LF) prediction. This raises the concern that semantically irrelevant details of the chosen LFs could shape model performance. We argue that this concern is realized for the COGS benchmark. COGS poses generalization splits that appear impossible for present-day models, which could be taken as an indictment of those models. However, we show that the negative results trace to incidental features of COGS LFs. Converting these LFs to semantically equivalent ones and factoring out capabilities unrelated to semantic interpretation, we find that even baseline models get traction. A recent variable-free translation of COGS LFs suggests similar conclusions, but we observe this format is not semantically equivalent; it is incapable of accurately representing some COGS meanings. These findings inform our proposal for ReCOGS, a modified version of COGS that comes closer to assessing the target semantic capabilities while remaining very challenging. Overall, our results reaffirm the importance of compositional generalization and careful benchmark task design.
Mordecai 3: A Neural Geoparser and Event Geocoder
Authors: Andrew Halterman
Date: 2023-03-23 00:00:00
Tasks: Question Answering, Toponym Resolution

Mordecai3 is a new end-to-end text geoparser and event geolocation system. The system performs toponym resolution using a new neural ranking model to resolve a place name extracted from a document to its entry in the Geonames gazetteer. It also performs event geocoding, the process of linking events reported in text with the place names where they are reported to occur, using an off-the-shelf question-answering model. The toponym resolution model is trained on a diverse set of existing training data, along with several thousand newly annotated examples. The paper describes the model, its training process, and performance comparisons with existing geoparsers. The system is available as an open source Python library, Mordecai 3, and replaces an earlier geoparser, Mordecai v2, one of the most widely used text geoparsers (Halterman 2017).
Multi-Antenna Dual-Blind Deconvolution for Joint Radar-Communications via SoMAN Minimization
Authors: Roman Jacome, Edwin Vargas, Kumar Vijay Mishra, Brian M. Sadler, Henry Arguello
Date: 2023-03-23 00:00:00

In joint radar-communications (JRC) applications such as secure military receivers, often the radar and communications signals are overlaid in the received signal. In these passive listening outposts, the signals and channels of both radar and communications are unknown to the receiver. The ill-posed problem of recovering all signal and channel parameters from the overlaid signal is termed as \textit{dual-blind deconvolution} (DBD). In this work, we investigate DBD for a multi-antenna receiver. We model the radar and communications channels with a few (sparse) \textit{continuous-valued} parameters such as time delays, Doppler velocities, and directions-of-arrival (DoAs). To solve this highly ill-posed DBD, we propose to minimize the sum of multivariate atomic norms (SoMAN) that depend on unknown parameters. To this end, we devise an exact semidefinite program using theories of positive hyperoctant trigonometric polynomials (PhTP). Our theoretical analyses show that the minimum number of samples and antennas required for perfect recovery is logarithmically dependent on the maximum of the number of radar targets and communications paths rather than their sum. We show that our approach is easily generalized to include several practical issues such as gain/phase errors and additive noise. Numerical experiments show the exact parameter recovery for different JRC scenarios.
Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisited
Authors: Zheng Yuan, Fajie Yuan, Yu Song, Youhua Li, Junchen Fu, Fei Yang, Yunzhu Pan, Yongxin Ni
Date: 2023-03-24 00:00:00
Tasks: Recommendation Systems

Recommendation models that utilize unique identities (IDs) to represent distinct users and items have been state-of-the-art (SOTA) and dominated the recommender systems (RS) literature for over a decade. Meanwhile, the pre-trained modality encoders, such as BERT and ViT, have become increasingly powerful in modeling the raw modality features of an item, such as text and images. Given this, a natural question arises: can a purely modality-based recommendation model (MoRec) outperforms or matches a pure ID-based model (IDRec) by replacing the itemID embedding with a SOTA modality encoder? In fact, this question was answered ten years ago when IDRec beats MoRec by a strong margin in both recommendation accuracy and efficiency. We aim to revisit this `old' question and systematically study MoRec from several aspects. Specifically, we study several sub-questions: (i) which recommendation paradigm, MoRec or IDRec, performs better in practical scenarios, especially in the general setting and warm item scenarios where IDRec has a strong advantage? does this hold for items with different modality features? (ii) can the latest technical advances from other communities (i.e., natural language processing and computer vision) translate into accuracy improvement for MoRec? (iii) how to effectively utilize item modality representation, can we use it directly or do we have to adjust it with new data? (iv) are there some key challenges for MoRec to be solved in practical applications? To answer them, we conduct rigorous experiments for item recommendations with two popular modalities, i.e., text and vision. We provide the first empirical evidence that MoRec is already comparable to its IDRec counterpart with an expensive end-to-end training method, even for warm item recommendation. Our results potentially imply that the dominance of IDRec in the RS field may be greatly challenged in the future.
Help the Blind See: Assistance for the Visually Impaired through Augmented Acoustic Simulation
Authors: Alexander Mehta, Ritik Jalisatgi
Date: 2023-02-09 00:00:00
Tasks: object-detection, Object Detection, Real-Time Object Detection

An estimated 253 million people have visual impairments. These visual impairments affect everyday lives, and limit their understanding of the outside world. This can pose a risk to health from falling or collisions. We propose a solution to this through quick and detailed communication of environmental spatial geometry through sound, providing the blind and visually impaired the ability to understand their spatial environment through sound technology. The model consists of fast object detection and 3D environmental mapping, which is communicated through a series of quick sound notes. These sound notes are at different frequencies, pitches, and arrangements in order to precisely communicate the depth and location of points within the environment. Sounds are communicated in the form of musical notes in order to be easily recognizable and distinguishable. A unique algorithm is used to segment objects, providing minimal accuracy loss and improvement from the normal O(n2 ) to O(n) (which is significant, as N in point clouds can often be in the range of 105 ). In testing, we achieved an R-value of 0.866 on detailed objects and an accuracy of 87.5% on an outdoor scene at night with large amounts of noise. We also provide a supplementary video demo of our system.
A Maximal Correlation Framework for Fair Machine Learning
Authors: Joshua Lee, Yuheng Bu, Prasanna Sattigeri, Rameswar Panda, Gregory W. Wornell, Leonid Karlinsky and Rogerio Schmidt Feris
Date: 2022-03-26 00:00:00
Proceeding: Entropy 2022 3
Tasks: Fairness

As machine learning algorithms grow in popularity and diversify to many industries, ethical and legal concerns regarding their fairness have become increasingly relevant. We explore the problem of algorithmic fairness, taking an information–theoretic view. The maximal correlation framework is introduced for expressing fairness constraints and is shown to be capable of being used to derive regularizers that enforce independence and separation-based fairness criteria, which admit optimization algorithms for both discrete and continuous variables that are more computationally efficient than existing algorithms. We show that these algorithms provide smooth performancefairness tradeoff curves and perform competitively with state-of-the-art methods on both discrete datasets (COMPAS, Adult) and continuous datasets (Communities and Crimes).
Embedding Pose Information for Multiview Vehicle Model Recognition
Authors: Ye Yu; Haitao Liu; Yuanzi Fu; Wei Jia; Jun Yu; Zhisheng Yan
Date: 2022-02-11 00:00:00
Proceeding: IEEE Transactions on Circuits and Systems for Video Technology 2022 2
Tasks: Classification, model, Pose Estimation

Vehicle model recognition is a typical fine-grained classification task that has a wide range of application prospects in safe cities and constitutes a research hotspot in the field of computer vision. Vehicles in images can appear at various angles, resulting in large differences in appearance. The existence of “multiviews” renders vehicle model recognition challenging. Recent research on vehicle model recognition has not fully explored the pose information of vehicles in different images, resulting in low model performance. In this study, we use vehicle pose information to solve the multiview vehicle model recognition (MV-VMR) problem and design a convolutional neural network (CNN) model with embedded vehicle pose information, known as the embedding pose CNN (EP-CNN). The proposed model includes two subnetworks: the pose estimation subnetwork (PE-SubNet) and vehicle model classification subnetwork (VMC-SubNet). PE-SubNet extracts the vehicle pose information, including the pose features and vehicle viewpoint. In VMC-SubNet, considering the scale variation of vehicles, an improved squeeze-and-excitation (SE) block, named the MultiSE block is implemented. We embed the vehicle viewpoint into the MultiSE block, which reweighs each channel such that the extracted features elicit different responses to different viewpoints. Subsequently, the pose features and classification features are integrated for classification. Experiments are conducted on the benchmark CompCars web-nature and Stanford Cars datasets. The results demonstrate that the proposed EP-CNN method can achieve higher recognition accuracy than most classic CNN models and several state-of-the-art fine-grained vehicle model classification algorithms. Code has been made available at: https://github.com/HFUT-CV/EP-CNN .
Cross-Part Learning for Fine-Grained Image Classification
Authors: Man Liu; Chunjie Zhang; Huihui Bai; Riquan Zhang; Yao Zhao
Date: 2021-12-21 00:00:00
Proceeding: IEEE Transactions on Image Processing 2021 12
Tasks: Classification, Diversity, Fine-Grained Image Classification, image-classification, Image Classification

Recent techniques have achieved remarkable improvements depended on mining subtle yet distinctive features for fine-grained visual classification (FGVC). While prior works directly combine discriminative features extracted from different parts, we argue that the potential interactions between different parts and their abilities to category predictions should be taken into consideration, which enables significant parts to contribute more to the decision of the sub-category. To this end, we present a Cross-Part Convolutional Neural Network (CP-CNN) in a weakly supervised manner to explore cross-learning among multi-regional features. Specifically, the context transformer is implemented to encourage joint feature learning across different parts under the guidance of a navigator. The part with the highest confidence is regarded as a navigator to deliver distinguishing characteristics to the others with lower confidence while the complementary information is retained. To locate discriminative but subtle parts precisely, a part proposal generator (PPG) is designed with the feature enhancement blocks, through which complex scale variations caused by the viewpoint diversity can be effectively alleviated. Extensive experiments on three benchmark datasets demonstrate that our proposed method consistently outperforms existing state-of-the-art methods.
Ranking Aggregation with Interactive Feedback for Collaborative Person Re-identification
Authors: Ji Huang, Chao Liang, Yue Zhang, Zhongyuan Wang, Chunjie Zhang
Date: 2022-11-21 00:00:00
Proceeding: The 33rd British Machine Vision Conference 2022 11
Tasks: Person Re-Identification, Re-Ranking, Retrieval

Person re-identification (re-ID) aims to retrieve the same person from a group of networking cameras. Ranking aggregation (RA), a method to aggregates multiple ranking results, can further improve the retrieval accuracy in re-ID tasks. Existing RA work can be generally divided into unsupervised methods and fully-supervised methods. Unsupervised methods lack external supervision, can hardly achieve the optimal results. In contrast, fully-supervised methods need massive labeling data for training, which is prohibitively expensive in the practical application. This paper studies interactive RA (IRA) to address the above challenges in existing RA research. The core idea is to utilize a small amount of supervisory information, obtained from users' relevance feedback, to supervise RA method to produce better re-ranking results. Compared with unsupervised methods, IRA introduces supervisory information and thus has better aggregation accuracy. Compared with fully-supervision methods, the supervisory information of IRA is more readily available, and can be targeted to specific queries. Particularly, we propose two IRA implementations, based on ranking positions and scores respectively, to adapt to diverse application scenarios, where rankers only give rankings, or rankers give similarity scores. Experiments on three public re-ID datasets have shown that IRA significantly outperforms the-state-of-art unsupervised baselines, and achieves similar accuracy with less labeling cost than the fully-supervised RA method.
Balancing Transferability and Discriminability for Unsupervised Domain Adaptation.
Authors: Jingke Huang; Ni Xiao; Lei Zhang
Date: 2022-09-15 00:00:00
Proceeding: IEEE Transactions on Neural Networks and Learning Systems 2022 9
Tasks: Domain Adaptation, Representation Learning, Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) aims to leverage a sufficiently labeled source domain to classify or represent the fully unlabeled target domain with a different distribution. Generally, the existing approaches try to learn a domain-invariant representation for feature transferability and add class discriminability constraints for feature discriminability. However, the feature transferability and discriminability are usually not synchronized, and there are even some contradictions between them, which is often ignored and, thus, reduces the accuracy of recognition. In this brief, we propose a deep multirepresentations adversarial learning (DMAL) method to explore and mitigate the inconsistency between feature transferability and discriminability in UDA task. Specifically, we consider feature representation learning at both the domain level and class level and explore four types of feature representations: domain-invariant, domain-specific, class-invariant, and class-specific. The first two types indicate the transferability of features, and the last two indicate the discriminability. We develop an adversarial learning strategy between the four representations to make the feature transferability and discriminability to be gradually synchronized. A series of experimental results verify that the proposed DMAL achieves comparable and promising results on six UDA datasets.
Compound Projection Learning for Bridging Seen and Unseen Objects
Authors: Wenli Song; Lei Zhang; Xinbo Gao
Date: 2022-01-22 00:00:00
Proceeding: IEEE Transactions on Multimedia 2022 1
Tasks: Zero-Shot Learning

Zero-shot Learning (ZSL) aims to transfer knowledge from seen image categories to unseen ones by leveraging semantic information. It is generally assumed that the seen and unseen objects share a common semantic space. Most of existing ZSL methods focus on how to connect the visual space and the semantic space. However, since there are some visual distribution differences between seen and unseen objects, the projection function learned by those seen classes is biased when transferring knowledge to unseen classes. We argue that, although the unseen objects are class-agnostic, the visual distribution information of unseen samples can be generated by exploiting semantic features. In this paper, we propose a \underline{C}ompound \underline{P}rojection \underline{L}earning (CPL) model to transfer knowledge from seen to unseen objects by exploiting the information of both seen and class-agnostic samples. With the projected semantic representation by CPL, effective constraints such as projection loss and semantic reconstruction loss can be explored for seen and unseen objects, respectively, such that the semantic ambiguity across seen and unseen objects is reduced. Additionally, we utilize a similarity network to further explore the inter-class relationship by employing the labels and the similarities between seen and unseen classes. Extensive experiments on three benchmark datasets show the effectiveness of our proposed approach.
Window Size Selection in Unsupervised Time Series Analytics: A Review and Benchmark
Authors: Arik Ermshaus, Patrick Schäfer, Ulf Leser
Date: 2023-02-04 00:00:00
Proceeding: Advanced Analytics and Learning on Temporal Data 2023 2
Tasks: Anomaly Detection, Change Point Detection, Hyperparameter Optimization, Semantic Segmentation, Time Series, Time Series Analysis, Time Series Anomaly Detection

Time series (TS) are sequences of values ordered in time. Such TS have in common, that important insights from the data can be drawn by inspecting local substructures, and not the recordings as a whole. ECG recordings, for instance, are characterized by normal or anomalous heartbeats that repeat themselves often within a longer TS. As such, many state-of-the-art time series data mining (TSDM) methods characterize TS by inspecting local substructures. The window size for extracting such subsequences is a crucial hyper-parameter, and setting an inappropriate value results in poor TSDM results. Finding the optimal window size has remained to be one of the most challenging tasks in TSDM domains, where no domain-agnostic method is known for learning the window size. We provide, for the first time, a systematic survey and experimental study of 6 TS window size selection (WSS) algorithms on three diverse TSDM tasks, namely anomaly detection, segmentation and motif discovery, using state-of-the art TSDM algorithms and benchmarks. We found that WSS methods are competitive with or even surpass human annotations, if an interesting or anomalous pattern can be attributed to (changes in) the period. That is because current WSS methods aim at finding the period length of data sets. This assumption is mostly true for segmentation or anomaly detection, by definition. In the case of motif discovery, however, the results were mixed. Motifs can be independent of a period, but repeat themselves unusually often. In this domain, WSS fails and more research is needed.