pwc-archive/papers-with-abstracts huggingface dataset:
On the Relationship Between Counterfactual Explainer and Recommender
Authors: Gang Liu, Zhihan Zhang, Zheng Ning, Meng Jiang
Date: 2022-07-09 00:00:00
Tasks: Collaborative Filtering, counterfactual, Counterfactual Explanation, Recommendation Systems

Recommender systems employ machine learning models to learn from historical data to predict the preferences of users. Deep neural network (DNN) models such as neural collaborative filtering (NCF) are increasingly popular. However, the tangibility and trustworthiness of the recommendations are questionable due to the complexity and lack of explainability of the models. To enable explainability, recent techniques such as ACCENT and FIA are looking for counterfactual explanations that are specific historical actions of a user, the removal of which leads to a change to the recommendation result. In this work, we present a general framework for both DNN and non-DNN models so that the counterfactual explainers all belong to it with specific choices of components. This framework first estimates the influence of a certain historical action after its removal and then uses search algorithms to find the minimal set of such actions for the counterfactual explanation. With this framework, we are able to investigate the relationship between the explainers and recommenders. We empirically study two recommender models (NCF and Factorization Machine) and two datasets (MovieLens and Yelp). We analyze the relationship between the performance of the recommender and the quality of the explainer. We observe that with standard evaluation metrics, the explainers deliver worse performance when the recommendations are more accurate. This indicates that having good explanations to correct predictions is harder than having them to wrong predictions. The community needs more fine-grained evaluation metrics to measure the quality of counterfactual explanations to recommender systems.
Adaptive Structural Similarity Preserving for Unsupervised Cross Modal Hashing
Authors: Liang Li, Baihua Zheng, Weiwei Sun
Date: 2022-07-09 00:00:00
Tasks: Management, Representation Learning

Cross-modal hashing is an important approach for multimodal data management and application. Existing unsupervised cross-modal hashing algorithms mainly rely on data features in pre-trained models to mine their similarity relationships. However, their optimization objectives are based on the static metric between the original uni-modal features, without further exploring data correlations during the training. In addition, most of them mainly focus on association mining and alignment among pairwise instances in continuous space but ignore the latent structural correlations contained in the semantic hashing space. In this paper, we propose an unsupervised hash learning framework, namely Adaptive Structural Similarity Preservation Hashing (ASSPH), to solve the above problems. Firstly, we propose an adaptive learning scheme, with limited data and training batches, to enrich semantic correlations of unlabeled instances during the training process and meanwhile to ensure a smooth convergence of the training process. Secondly, we present an asymmetric structural semantic representation learning scheme. We introduce structural semantic metrics based on graph adjacency relations during the semantic reconstruction and correlation mining stage and meanwhile align the structure semantics in the hash space with an asymmetric binary optimization process. Finally, we conduct extensive experiments to validate the enhancements of our work in comparison with existing works.
Explainable Legal Case Matching via Inverse Optimal Transport-based Rationale Extraction
Authors: Weijie Yu, Zhongxiang Sun, Jun Xu, Zhenhua Dong, Xu Chen, Hongteng Xu, Ji-Rong Wen
Date: 2022-07-09 00:00:00
Tasks: Explanation Generation, Fairness, Retrieval, Semantic Similarity, Semantic Textual Similarity, Sentence

As an essential operation of legal retrieval, legal case matching plays a central role in intelligent legal systems. This task has a high demand on the explainability of matching results because of its critical impacts on downstream applications -- the matched legal cases may provide supportive evidence for the judgments of target cases and thus influence the fairness and justice of legal decisions. Focusing on this challenging task, we propose a novel and explainable method, namely \textit{IOT-Match}, with the help of computational optimal transport, which formulates the legal case matching problem as an inverse optimal transport (IOT) problem. Different from most existing methods, which merely focus on the sentence-level semantic similarity between legal cases, our IOT-Match learns to extract rationales from paired legal cases based on both semantics and legal characteristics of their sentences. The extracted rationales are further applied to generate faithful explanations and conduct matching. Moreover, the proposed IOT-Match is robust to the alignment label insufficiency issue commonly in practical legal case matching tasks, which is suitable for both supervised and semi-supervised learning paradigms. To demonstrate the superiority of our IOT-Match method and construct a benchmark of explainable legal case matching task, we not only extend the well-known Challenge of AI in Law (CAIL) dataset but also build a new Explainable Legal cAse Matching (ELAM) dataset, which contains lots of legal cases with detailed and explainable annotations. Experiments on these two datasets show that our IOT-Match outperforms state-of-the-art methods consistently on matching prediction, rationale extraction, and explanation generation.
MMNet: A Model-Based Multimodal Network for Human Action Recognition in RGB-D Videos
Authors: Bruce X.B. Yu, Yan Liu, Xiang Zhang, Sheng-hua Zhong, Keith C.C. Chan
Date: 2022-05-26 00:00:00
Proceeding: IEEE Transactions on Pattern Analysis and Machine Intelligence 2022 5
Tasks: Action Classification, Action Recognition, Action Recognition In Videos, Skeleton Based Action Recognition, Temporal Action Localization

Human action recognition (HAR) in RGB-D videos has been widely investigated since the release of affordable depth
sensors. Currently, unimodal approaches (e.g., skeleton-based and RGB video-based) have realized substantial improvements with
increasingly larger datasets. However, multimodal methods specifically with model-level fusion have seldom been investigated. In this
paper, we propose a model-based multimodal network (MMNet) that fuses skeleton and RGB modalities via a model-based approach.
The objective of our method is to improve ensemble recognition accuracy by effectively applying mutually complementary information
from different data modalities. For the model-based fusion scheme, we use a spatiotemporal graph convolution network for the skeleton modality to learn attention weights that will be transferred to the network of the RGB modality. Extensive experiments are conducted on five benchmark datasets: NTU RGB+D 60, NTU RGB+D 120, PKU-MMD, Northwestern-UCLA Multiview, and Toyota Smarthome. Upon aggregating the results of multiple modalities, our method is found to outperform state-of-the-art approaches on six evaluation protocols of the five datasets; thus, the proposed MMNet can effectively capture mutually complementary features in different RGB-D video modalities and provide more discriminative features for HAR. We also tested our MMNet on an RGB video dataset Kinetics 400 that contains more outdoor actions, which shows consistent results with those of RGB-D video datasets.
Multimodal Fusion via Teacher-Student Network for Indoor Action Recognition
Authors: Bruce X.B. Yu, Yan Liu, Keith C.C. Chan
Date: 2021-05-18 00:00:00
Proceeding: Association for the Advancement of Artificial Intelligence (AAAI) 2021 5
Tasks: Action Recognition, Action Recognition In Videos, Benchmarking

Indoor action recognition plays an important role in modern
society, such as intelligent healthcare in large mobile cabin
hospitals. With the wide usage of depth sensors like Kinect,
multimodal information including skeleton and RGB modalities
brings a promising way to improve the performance.
However, existing methods are either focusing on a single
data modality or failed to take the advantage of multiple data
modalities. In this paper, we propose a Teacher-Student Multimodal
Fusion (TSMF) model that fuses the skeleton and
RGB modalities at the model level for indoor action recognition.
In our TSMF, we utilize a teacher network to transfer
the structural knowledge of the skeleton modality to a
student network for the RGB modality. With extensive experiments
on two benchmarking datasets: NTU RGB+D and
PKU-MMD, results show that the proposed TSMF consistently
performs better than state-of-the-art single modal and
multimodal methods. It also indicates that our TSMF could
not only improve the accuracy of the student network but also
significantly improve the ensemble accuracy.
Sequence Alignment Ensemble with a Single Neural Network for Sequence Labeling
Authors: Jeesu Jung, SangKeun Jung, Hyein Seo, Hyuk Namgung, SungRyeol Kim
Date: 2022-07-07 00:00:00
Proceeding: IEEE Access 2022 7
Tasks: Part-Of-Speech Tagging, POS, POS Tagging

Sequence labeling, in which a class or label is assigned to each token in a given input order, is a fundamental task in natural language processing. Many advanced neural network architectures have recently been proposed to solve the sequential labeling problem affecting this task. By contrast, only a few approaches have been proposed to address the sequential ensemble problem. In this paper, we resolve the sequential ensemble problem by applying the sequential alignment method in a proposed ensemble framework. Specifically, we propose a simple but efficient ensemble candidate generation framework with which multiple heterogeneous systems can easily be prepared from a single neural sequence labeling network. To evaluate the proposed framework, experiments were conducted with part-of-speech (POS) tagging and dependency label prediction problems. The results indicate that the proposed framework achieved accuracy values that were higher by 0.19 and 0.33 than those achieved by the hard-voting method on the Penn-treebank POS-tagged and Universal dependency-tagged datasets, respectively.
Customer Segmentation
Authors: VIJAY CHOUDHARY
Date: 2018-02-15 00:00:00
Proceeding: From Udemy's Machine Learning A-Z course 2018 2
Tasks: BIG-bench Machine Learning, Clustering, Segmentation

This data set is created only for the learning purpose of the customer segmentation concepts , also known as market basket analysis . I will demonstrate this by using unsupervised ML technique (KMeans Clustering Algorithm) in the simplest form.

Malls or shopping complexes are often indulged in the race to increase their customers and hence making huge profits. To achieve this task machine learning is being applied by many stores already.
It is amazing to realize the fact that how machine learning can aid in such ambitions. The shopping complexes make use of their customers’ data and develop ML models to target the right ones. This not only increases sales but also makes the complexes efficient.
Fine-grained Activities of People Worldwide
Authors: Jeffrey Byrne, Greg Castanon, Zhongheng Li, Gil Ettinger
Date: 2022-07-11 00:00:00
Tasks: Action Detection, Activity Detection, Activity Recognition, Diversity

Every day, humans perform many closely related activities that involve subtle discriminative motions, such as putting on a shirt vs. putting on a jacket, or shaking hands vs. giving a high five. Activity recognition by ethical visual AI could provide insights into our patterns of daily life, however existing activity recognition datasets do not capture the massive diversity of these human activities around the world. To address this limitation, we introduce Collector, a free mobile app to record video while simultaneously annotating objects and activities of consented subjects. This new data collection platform was used to curate the Consented Activities of People (CAP) dataset, the first large-scale, fine-grained activity dataset of people worldwide. The CAP dataset contains 1.45M video clips of 512 fine grained activity labels of daily life, collected by 780 subjects in 33 countries. We provide activity classification and activity detection benchmarks for this dataset, and analyze baseline results to gain insight into how people around with world perform common activities. The dataset, benchmarks, evaluation tools, public leaderboards and mobile apps are available for use at visym.github.io/cap.
Overview of the Shared Task on Fake News Detection in Urdu at FIRE 2021
Authors: Maaz Amjad, Sabur Butt, Hamza Imam Amjad, Alisa Zhila, Grigori Sidorov, Alexander Gelbukh
Date: 2022-07-11 00:00:00
Tasks: Articles, Binary Classification, Fake News Detection

Automatic detection of fake news is a highly important task in the contemporary world. This study reports the 2nd shared task called UrduFake@FIRE2021 on identifying fake news detection in Urdu. The goal of the shared task is to motivate the community to come up with efficient methods for solving this vital problem, particularly for the Urdu language. The task is posed as a binary classification problem to label a given news article as a real or a fake news article. The organizers provide a dataset comprising news in five domains: (i) Health, (ii) Sports, (iii) Showbiz, (iv) Technology, and (v) Business, split into training and testing sets. The training set contains 1300 annotated news articles -- 750 real news, 550 fake news, while the testing set contains 300 news articles -- 200 real, 100 fake news. 34 teams from 7 different countries (China, Egypt, Israel, India, Mexico, Pakistan, and UAE) registered to participate in the UrduFake@FIRE2021 shared task. Out of those, 18 teams submitted their experimental results, and 11 of those submitted their technical reports, which is substantially higher compared to the UrduFake shared task in 2020 when only 6 teams submitted their technical reports. The technical reports submitted by the participants demonstrated different data representation techniques ranging from count-based BoW features to word vector embeddings as well as the use of numerous machine learning algorithms ranging from traditional SVM to various neural network architectures including Transformers such as BERT and RoBERTa. In this year's competition, the best performing system obtained an F1-macro score of 0.679, which is lower than the past year's best result of 0.907 F1-macro. Admittedly, while training sets from the past and the current years overlap to a large extent, the testing set provided this year is completely different.
UrduFake@FIRE2021: Shared Track on Fake News Identification in Urdu
Authors: Maaz Amjad, Sabur Butt, Hamza Imam Amjad, Grigori Sidorov, Alisa Zhila, Alexander Gelbukh
Date: 2022-07-11 00:00:00
Tasks: Binary Classification, Fake News Detection

This study reports the second shared task named as UrduFake@FIRE2021 on identifying fake news detection in Urdu language. This is a binary classification problem in which the task is to classify a given news article into two classes: (i) real news, or (ii) fake news. In this shared task, 34 teams from 7 different countries (China, Egypt, Israel, India, Mexico, Pakistan, and UAE) registered to participate in the shared task, 18 teams submitted their experimental results and 11 teams submitted their technical reports. The proposed systems were based on various count-based features and used different classifiers as well as neural network architectures. The stochastic gradient descent (SGD) algorithm outperformed other classifiers and achieved 0.679 F-score.
Towards Neural Numeric-To-Text Generation From Temporal Personal Health Data
Authors: Jonathan Harris, Mohammed J. Zaki
Date: 2022-07-11 00:00:00
Tasks: Data Summarization, Decoder, Text Generation, Time Series, Time Series Analysis

With an increased interest in the production of personal health technologies designed to track user data (e.g., nutrient intake, step counts), there is now more opportunity than ever to surface meaningful behavioral insights to everyday users in the form of natural language. This knowledge can increase their behavioral awareness and allow them to take action to meet their health goals. It can also bridge the gap between the vast collection of personal health data and the summary generation required to describe an individual's behavioral tendencies. Previous work has focused on rule-based time-series data summarization methods designed to generate natural language summaries of interesting patterns found within temporal personal health data. We examine recurrent, convolutional, and Transformer-based encoder-decoder models to automatically generate natural language summaries from numeric temporal personal health data. We showcase the effectiveness of our models on real user health data logged in MyFitnessPal and show that we can automatically generate high-quality natural language summaries. Our work serves as a first step towards the ambitious goal of automatically generating novel and meaningful temporal summaries from personal health data.
Language Models (Mostly) Know What They Know
Authors: Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield-Dodds, Nova DasSarma, Eli Tran-Johnson, Scott Johnston, Sheer El-Showk, Andy Jones, Nelson Elhage, Tristan Hume, Anna Chen, Yuntao Bai, Sam Bowman, Stanislav Fort, Deep Ganguli, Danny Hernandez, Josh Jacobson, Jackson Kernion, Shauna Kravec, Liane Lovitt, Kamal Ndousse, Catherine Olsson, Sam Ringer, Dario Amodei, Tom Brown, Jack Clark, Nicholas Joseph, Ben Mann, Sam McCandlish, Chris Olah, Jared Kaplan
Date: 2022-07-11 00:00:00
Tasks: Multiple-choice

We study whether language models can evaluate the validity of their own claims and predict which questions they will be able to answer correctly. We first show that larger models are well-calibrated on diverse multiple choice and true/false questions when they are provided in the right format. Thus we can approach self-evaluation on open-ended sampling tasks by asking models to first propose answers, and then to evaluate the probability "P(True)" that their answers are correct. We find encouraging performance, calibration, and scaling for P(True) on a diverse array of tasks. Performance at self-evaluation further improves when we allow models to consider many of their own samples before predicting the validity of one specific possibility. Next, we investigate whether models can be trained to predict "P(IK)", the probability that "I know" the answer to a question, without reference to any particular proposed answer. Models perform well at predicting P(IK) and partially generalize across tasks, though they struggle with calibration of P(IK) on new tasks. The predicted P(IK) probabilities also increase appropriately in the presence of relevant source materials in the context, and in the presence of hints towards the solution of mathematical word problems. We hope these observations lay the groundwork for training more honest models, and for investigating how honesty generalizes to cases where models are trained on objectives other than the imitation of human writing.
Bootstrapping a User-Centered Task-Oriented Dialogue System
Authors: Shijie Chen, Ziru Chen, Xiang Deng, Ashley Lewis, Lingbo Mo, Samuel Stevens, Zhen Wang, Xiang Yue, Tianshu Zhang, Yu Su, Huan Sun
Date: 2022-07-11 00:00:00
Tasks: Data Augmentation, Dialogue Management, Management, Response Generation

We present TacoBot, a task-oriented dialogue system built for the inaugural Alexa Prize TaskBot Challenge, which assists users in completing multi-step cooking and home improvement tasks. TacoBot is designed with a user-centered principle and aspires to deliver a collaborative and accessible dialogue experience. Towards that end, it is equipped with accurate language understanding, flexible dialogue management, and engaging response generation. Furthermore, TacoBot is backed by a strong search engine and an automated end-to-end test suite. In bootstrapping the development of TacoBot, we explore a series of data augmentation strategies to train advanced neural language processing models and continuously improve the dialogue experience with collected real conversations. At the end of the semifinals, TacoBot achieved an average rating of 3.55/5.0.
Building Korean Sign Language Augmentation (KoSLA) Corpus with Data Augmentation Technique
Authors: Changnam An, Eunkyung Han, Dongmyeong Noh, Ohkyoon Kwon, Sumi Lee, Hyunshim Han
Date: 2022-07-12 00:00:00
Tasks: Data Augmentation, Sign Language Translation, Translation

We present an efficient framework of corpus for sign language translation. Aided with a simple but dramatic data augmentation technique, our method converts text into annotated forms with minimum information loss. Sign languages are composed of manual signals, non-manual signals, and iconic features. According to professional sign language interpreters, non-manual signals such as facial expressions and gestures play an important role in conveying exact meaning. By considering the linguistic features of sign language, our proposed framework is a first and unique attempt to build a multimodal sign language augmentation corpus (hereinafter referred to as the KoSLA corpus) containing both manual and non-manual modalities. The corpus we built demonstrates confident results in the hospital context, showing improved performance with augmented datasets. To overcome data scarcity, we resorted to data augmentation techniques such as synonym replacement to boost the efficiency of our translation model and available data, while maintaining grammatical and semantic structures of sign language. For the experimental support, we verify the effectiveness of data augmentation technique and usefulness of our corpus by performing a translation task between normal sentences and sign language annotations on two tokenizers. The result was convincing, proving that the BLEU scores with the KoSLA corpus were significant.
A Survey on Table Question Answering: Recent Advances
Authors: Nengzheng Jin, Joanna Siebert, Dongfang Li, Qingcai Chen
Date: 2022-07-12 00:00:00
Tasks: Question Answering, Semantic Parsing, Survey

Table Question Answering (Table QA) refers to providing precise answers from tables to answer a user's question. In recent years, there have been a lot of works on table QA, but there is a lack of comprehensive surveys on this research topic. Hence, we aim to provide an overview of available datasets and representative methods in table QA. We classify existing methods for table QA into five categories according to their techniques, which include semantic-parsing-based, generative, extractive, matching-based, and retriever-reader-based methods. Moreover, as table QA is still a challenging task for existing methods, we also identify and outline several key challenges and discuss the potential future directions of table QA.