pwc-archive/papers-with-abstracts huggingface dataset:
The Style-Content Duality of Attractiveness: Learning to Write Eye-Catching Headlines via Disentanglement
Authors: Mingzhe Li, Xiuying Chen, Min Yang, Shen Gao, Dongyan Zhao, Rui Yan
Date: 2020-12-14 00:00:00
Tasks: Articles, Disentanglement

Eye-catching headlines function as the first device to trigger more clicks, bringing reciprocal effect between producers and viewers. Producers can obtain more traffic and profits, and readers can have access to outstanding articles. When generating attractive headlines, it is important to not only capture the attractive content but also follow an eye-catching written style. In this paper, we propose a Disentanglement-based Attractive Headline Generator (DAHG) that generates headline which captures the attractive content following the attractive style. Concretely, we first devise a disentanglement module to divide the style and content of an attractive prototype headline into latent spaces, with two auxiliary constraints to ensure the two spaces are indeed disentangled. The latent content information is then used to further polish the document representation and help capture the salient part. Finally, the generator takes the polished document as input to generate headline under the guidance of the attractive style. Extensive experiments on the public Kuaibao dataset show that DAHG achieves state-of-the-art performance. Human evaluation also demonstrates that DAHG triggers 22% more clicks than existing models.
Machine Learning to study the impact of gender-based violence in the news media
Authors: Hugo J. Bello, Nora Palomar, Elisa Gallego, Lourdes Jiménez Navascués, Celia Lozano
Date: 2020-11-27 00:00:00
Tasks: BIG-bench Machine Learning

While it remains a taboo topic, gender-based violence (GBV) undermines the health, dignity, security and autonomy of its victims. Many factors have been studied to generate or maintain this kind of violence, however, the influence of the media is still uncertain. Here, we use Machine Learning tools to extrapolate the effect of the news in GBV. By feeding neural networks with news, the topic information associated with each article can be recovered. Our findings show a relationship between GBV news and public awareness, the effect of mediatic GBV cases, and the intrinsic thematic relationship of GBV news. Because the used neural model can be easily adjusted, this also allows us to extend our approach to other media sources or topics
Exploiting BERT to improve aspect-based sentiment analysis performance on Persian language
Authors: H. Jafarian, A. H. Taghavi, A. Javaheri, R. Rawassizadeh
Date: 2020-12-02 00:00:00
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Natural Language Inference, Sentence, Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a more detailed task in sentiment analysis, by identifying opinion polarity toward a certain aspect in a text. This method is attracting more attention from the community, due to the fact that it provides more thorough and useful information. However, there are few language-specific researches on Persian language. The present research aims to improve the ABSA on the Persian Pars-ABSA dataset. This research shows the potential of using pre-trained BERT model and taking advantage of using sentence-pair input on an ABSA task. The results indicate that employing Pars-BERT pre-trained model along with natural language inference auxiliary sentence (NLI-M) could boost the ABSA task accuracy up to 91% which is 5.5% (absolute) higher than state-of-the-art studies on Pars-ABSA dataset.
Data-Driven Regular Expressions Evolution for Medical Text Classification Using Genetic Programming
Authors: J Liu, R Bai, Z Lu, P Ge, D Liu, Uwe Aickelin
Date: 2020-12-04 00:00:00
Tasks: Classification, General Classification, text-classification, Text Classification

In medical fields, text classification is one of the most important tasks that can significantly reduce human workload through structured information digitization and intelligent decision support. Despite the popularity of learning-based text classification techniques, it is hard for human to understand or manually fine-tune the classification results for better precision and recall, due to the black box nature of learning. This study proposes a novel regular expression-based text classification method making use of genetic programming (GP) approaches to evolve regular expressions that can classify a given medical text inquiry with satisfactory precision and recall while allow human to read the classifier and fine-tune accordingly if necessary. Given a seed population of regular expressions (can be randomly initialized or manually constructed by experts), our method evolves a population of regular expressions according to chosen fitness function, using a novel regular expression syntax and a series of carefully chosen reproduction operators. Our method is evaluated with real-life medical text inquiries from an online healthcare provider and shows promising performance. More importantly, our method generates classifiers that can be fully understood, checked and updated by medical doctors, which are fundamentally crucial for medical related practices.
Meta learning to classify intent and slot labels with noisy few shot examples
Authors: Shang-Wen Li, Jason Krone, Shuyan Dong, Yi Zhang, Yaser Al-Onaizan
Date: 2020-11-30 00:00:00
Tasks: Benchmarking, intent-classification, Intent Classification, Meta-Learning, Spoken Language Understanding

Recently deep learning has dominated many machine learning areas, including spoken language understanding (SLU). However, deep learning models are notorious for being data-hungry, and the heavily optimized models are usually sensitive to the quality of the training examples provided and the consistency between training and inference conditions. To improve the performance of SLU models on tasks with noisy and low training resources, we propose a new SLU benchmarking task: few-shot robust SLU, where SLU comprises two core problems, intent classification (IC) and slot labeling (SL). We establish the task by defining few-shot splits on three public IC/SL datasets, ATIS, SNIPS, and TOP, and adding two types of natural noises (adaptation example missing/replacing and modality mismatch) to the splits. We further propose a novel noise-robust few-shot SLU model based on prototypical networks. We show the model consistently outperforms the conventional fine-tuning baseline and another popular meta-learning method, Model-Agnostic Meta-Learning (MAML), in terms of achieving better IC accuracy and SL F1, and yielding smaller performance variation when noises are present.
Fake News Detection in Social Media using Graph Neural Networks and NLP Techniques: A COVID-19 Use-case
Authors: Abdullah Hamid, Nasrullah Shiekh, Naina Said, Kashif Ahmad, Asma Gul, Laiq Hassan, Ala Al-Fuqaha
Date: 2020-11-30 00:00:00
Tasks: Binary Classification, Classification, Fake News Detection, General Classification, Misinformation

The paper presents our solutions for the MediaEval 2020 task namely FakeNews: Corona Virus and 5G Conspiracy Multimedia Twitter-Data-Based Analysis. The task aims to analyze tweets related to COVID-19 and 5G conspiracy theories to detect misinformation spreaders. The task is composed of two sub-tasks namely (i) text-based, and (ii) structure-based fake news detection. For the first task, we propose six different solutions relying on Bag of Words (BoW) and BERT embedding. Three of the methods aim at binary classification task by differentiating in 5G conspiracy and the rest of the COVID-19 related tweets while the rest of them treat the task as ternary classification problem. In the ternary classification task, our BoW and BERT based methods obtained an F1-score of .606% and .566% on the development set, respectively. On the binary classification, the BoW and BERT based solutions obtained an average F1-score of .666% and .693%, respectively. On the other hand, for structure-based fake news detection, we rely on Graph Neural Networks (GNNs) achieving an average ROC of .95% on the development set.
Procode: the Swiss Multilingual Solution for Automatic Coding and Recoding of Occupations and Economic Activities
Authors: Nenad Savic, Nicolas Bovio, Fabian Gilbert, Irina Guseva Canu
Date: 2020-11-30 00:00:00
Tasks: Classification, General Classification

Objective. Epidemiological studies require data that are in alignment with the classifications established for occupations or economic activities. The classifications usually include hundreds of codes and titles. Manual coding of raw data may result in misclassification and be time consuming. The goal was to develop and test a web-tool, named Procode, for coding of free-texts against classifications and recoding between different classifications. Methods. Three text classifiers, i.e. Complement Naive Bayes (CNB), Support Vector Machine (SVM) and Random Forest Classifier (RFC), were investigated using a k-fold cross-validation. 30 000 free-texts with manually assigned classification codes of French classification of occupations (PCS) and French classification of activities (NAF) were available. For recoding, Procode integrated a workflow that converts codes of one classification to another according to existing crosswalks. Since this is a straightforward operation, only the recoding time was measured. Results. Among the three investigated text classifiers, CNB resulted in the best performance, where the classifier predicted accurately 57-81% and 63-83% classification codes for PCS and NAF, respectively. SVM lead to somewhat lower results (by 1-2%), while RFC coded accurately up to 30% of the data. The coding operation required one minute per 10 000 records, while the recoding was faster, i.e. 5-10 seconds. Conclusion. The algorithm integrated in Procode showed satisfactory performance, since the tool had to assign the right code by choosing between 500-700 different choices. Based on the results, the authors decided to implement CNB in Procode. In future, if another classifier shows a superior performance, an update will include the required modifications.
Disentangling Homophemes in Lip Reading using Perplexity Analysis
Authors: Souheil Fenghour, Daqing Chen, Kun Guo, Perry Xiao
Date: 2020-11-28 00:00:00
Tasks: Language Modeling, Language Modelling, Lip Reading, Sentence, text-classification, Text Classification

The performance of automated lip reading using visemes as a classification schema has achieved less success compared with the use of ASCII characters and words largely due to the problem of different words sharing identical visemes. The Generative Pre-Training transformer is an effective autoregressive language model used for many tasks in Natural Language Processing, including sentence prediction and text classification. This paper proposes a new application for this model and applies it in the context of lip reading, where it serves as a language model to convert visual speech in the form of visemes, to language in the form of words and sentences. The network uses the search for optimal perplexity to perform the viseme-to-word mapping and is thus a solution to the one-to-many mapping problem that exists whereby various words that sound different when spoken look identical. This paper proposes a method to tackle the one-to-many mapping problem when performing automated lip reading using solely visual cues in two separate scenarios: the first scenario is where the word boundary, that is, the beginning and the ending of a word, is unknown; and the second scenario is where the boundary is known. Sentences from the benchmark BBC dataset "Lip Reading Sentences in the Wild"(LRS2), are classified with a character error rate of 10.7% and a word error rate of 18.0%. The main contribution of this paper is to propose a method of predicting words through the use of perplexity analysis when only visual cues are present, using an autoregressive language model.
Sentiment analysis in Bengali via transfer learning using multi-lingual BERT
Authors: Khondoker Ittehadul Islam, Md. Saiful Islam, Md Ruhul Amin
Date: 2020-12-03 00:00:00
Tasks: Sentiment Analysis, Sentiment Classification, Transfer Learning

Sentiment analysis (SA) in Bengali is challenging due to this Indo-Aryan language's highly inflected properties with more than 160 different inflected forms for verbs and 36 different forms for noun and 24 different forms for pronouns. The lack of standard labeled datasets in the Bengali domain makes the task of SA even harder. In this paper, we present manually tagged 2-class and 3-class SA datasets in Bengali. We also demonstrate that the multi-lingual BERT model with relevant extensions can be trained via the approach of transfer learning over those novel datasets to improve the state-of-the-art performance in sentiment classification tasks. This deep learning model achieves an accuracy of 71\% for 2-class sentiment classification compared to the current state-of-the-art accuracy of 68\%. We also present the very first Bengali SA classifier for the 3-class manually tagged dataset, and our proposed model achieves an accuracy of 60\%. We further use this model to analyze the sentiment of public comments in the online daily newspaper. Our analysis shows that people post negative comments for political or sports news more often, while the religious article comments represent positive sentiment. The dataset and code is publicly available at https://github.com/KhondokerIslam/Bengali\_Sentiment.
Towards unsupervised phone and word segmentation using self-supervised vector-quantized neural networks
Authors: Herman Kamper, Benjamin van Niekerk
Date: 2020-12-14 00:00:00
Tasks: Clustering, Segmentation

We investigate segmenting and clustering speech into low-bitrate phone-like sequences without supervision. We specifically constrain pretrained self-supervised vector-quantized (VQ) neural networks so that blocks of contiguous feature vectors are assigned to the same code, thereby giving a variable-rate segmentation of the speech into discrete units. Two segmentation methods are considered. In the first, features are greedily merged until a prespecified number of segments are reached. The second uses dynamic programming to optimize a squared error with a penalty term to encourage fewer but longer segments. We show that these VQ segmentation methods can be used without alteration across a wide range of tasks: unsupervised phone segmentation, ABX phone discrimination, same-different word discrimination, and as inputs to a symbolic word segmentation algorithm. The penalized dynamic programming method generally performs best. While performance on individual tasks is only comparable to the state-of-the-art in some cases, in all tasks a reasonable competing approach is outperformed at a substantially lower bitrate.
Large-scale Quantitative Evidence of Media Impact on Public Opinion toward China
Authors: Junming Huang, Gavin Cook, Yu Xie
Date: 2020-12-08 00:00:00
Tasks: Articles

Do mass media influence people's opinion of other countries? Using BERT, a deep neural network-based natural language processing model, we analyze a large corpus of 267,907 China-related articles published by The New York Times since 1970. We then compare our output from The New York Times to a longitudinal data set constructed from 101 cross-sectional surveys of the American public's views on China. We find that the reporting of The New York Times on China in one year explains 54% of the variance in American public opinion on China in the next. Our result confirms hypothesized links between media and public opinion and helps shed light on how mass media can influence public opinion of foreign countries.
Clickbait in Hindi News Media : A Preliminary Study
Authors: Vivek Kaushal, Kavita Vemuri
Date: 2020-12-14 00:00:00
Proceeding: ICON 2020 12
Tasks: POS, TAG

A corpus of Hindi news headlines shared on Twitter was created by collecting tweets of 5 mainstream Hindi news sources for a period of 4 months. 7 independent annotators were recruited to mark the 20 most retweeted news posts by each of the 5 news sources on its clickbait nature. The clickbait score hence generated was assessed for its correlation with interactions on the platform (retweets, favorites, reader replies), tweet word count, and normalized POS (part-of-speech) tag counts in tweets. A positive correlation was observed between readers' interactions with tweets and tweets' clickbait score. Significant correlations were also observed for POS tag counts and clickbait score. The prevalence of clickbait in mainstream Hindi news media was found to be similar to its prevalence in English news media. We hope that our observations would provide a platform for discussions on clickbait in mainstream Hindi news media.
Time to Transfer: Predicting and Evaluating Machine-Human Chatting Handoff
Authors: Jiawei Liu, Zhe Gao, Yangyang Kang, Zhuoren Jiang, Guoxiu He, Changlong Sun, Xiaozhong Liu, Wei Lu
Date: 2020-12-14 00:00:00
Tasks: Chatbot

Is chatbot able to completely replace the human agent? The short answer could be - "it depends...". For some challenging cases, e.g., dialogue's topical spectrum spreads beyond the training corpus coverage, the chatbot may malfunction and return unsatisfied utterances. This problem can be addressed by introducing the Machine-Human Chatting Handoff (MHCH), which enables human-algorithm collaboration. To detect the normal/transferable utterances, we propose a Difficulty-Assisted Matching Inference (DAMI) network, utilizing difficulty-assisted encoding to enhance the representations of utterances. Moreover, a matching inference mechanism is introduced to capture the contextual matching features. A new evaluation metric, Golden Transfer within Tolerance (GT-T), is proposed to assess the performance by considering the tolerance property of the MHCH. To provide insights into the task and validate the proposed model, we collect two new datasets. Extensive experimental results are presented and contrasted against a series of baseline models to demonstrate the efficacy of our model on MHCH.
The Complexity of Comparative Text Analysis -- "The Gardener is always the Murderer" says the Fourth Machine
Authors: Marcus Weber, Konstantin Fackeldey
Date: 2020-12-11 00:00:00

There is a heated debate about how far computers can map the complexity of text analysis compared to the abilities of the whole team of human researchers. A "deep" analysis of a given text is still beyond the possibilities of modern computers. In the heart of the existing computational text analysis algorithms there are operations with real numbers, such as additions and multiplications according to the rules of algebraic fields. However, the process of "comparing" has a very precise mathematical structure, which is different from the structure of an algebraic field. The mathematical structure of "comparing" can be expressed by using Boolean rings. We build on this structure and define the corresponding algebraic equations lifting algorithms of comparative text analysis onto the "correct" algebraic basis. From this point of view, we can investigate the question of {\em computational} complexity of comparative text analysis.
Unsupervised Opinion Summarization with Content Planning
Authors: Reinald Kim Amplayo, Stefanos Angelidis, Mirella Lapata
Date: 2020-12-14 00:00:00
Tasks: Abstractive Text Summarization, Opinion Summarization, Unsupervised Opinion Summarization

The recent success of deep learning techniques for abstractive summarization is predicated on the availability of large-scale datasets. When summarizing reviews (e.g., for products or movies), such training data is neither available nor can be easily sourced, motivating the development of methods which rely on synthetic datasets for supervised training. We show that explicitly incorporating content planning in a summarization model not only yields output of higher quality, but also allows the creation of synthetic datasets which are more natural, resembling real world document-summary pairs. Our content plans take the form of aspect and sentiment distributions which we induce from data without access to expensive annotations. Synthetic datasets are created by sampling pseudo-reviews from a Dirichlet distribution parametrized by our content planner, while our model generates summaries based on input reviews and induced content plans. Experimental results on three domains show that our approach outperforms competitive models in generating informative, coherent, and fluent summaries that capture opinion consensus.
Audio Captioning using Pre-Trained Large-Scale Language Model Guided by Audio-based Similar Caption Retrieval
Authors: Yuma Koizumi, Yasunori Ohishi, Daisuke Niizumi, Daiki Takeuchi, Masahiro Yasuda
Date: 2020-12-14 00:00:00
Tasks: Audio captioning, Language Modeling, Language Modelling, Retrieval

The goal of audio captioning is to translate input audio into its description using natural language. One of the problems in audio captioning is the lack of training data due to the difficulty in collecting audio-caption pairs by crawling the web. In this study, to overcome this problem, we propose to use a pre-trained large-scale language model. Since an audio input cannot be directly inputted into such a language model, we utilize guidance captions retrieved from a training dataset based on similarities that may exist in different audio. Then, the caption of the audio input is generated by using a pre-trained language model while referring to the guidance captions. Experimental results show that (i) the proposed method has succeeded to use a pre-trained language model for audio captioning, and (ii) the oracle performance of the pre-trained model-based caption generator was clearly better than that of the conventional method trained from scratch.
Towards Fairness in Classifying Medical Conversations into SOAP Sections
Authors: Elisa Ferracane, Sandeep Konam
Date: 2020-12-02 00:00:00
Tasks: Fairness

As machine learning algorithms are more widely deployed in healthcare, the question of algorithmic fairness becomes more critical to examine. Our work seeks to identify and understand disparities in a deployed model that classifies doctor-patient conversations into sections of a medical SOAP note. We employ several metrics to measure disparities in the classifier performance, and find small differences in a portion of the disadvantaged groups. A deeper analysis of the language in these conversations and further stratifying the groups suggests these differences are related to and often attributable to the type of medical appointment (e.g., psychiatric vs. internist). Our findings stress the importance of understanding the disparities that may exist in the data itself and how that affects a model's ability to equally distribute benefits.
Incremental Data-driven Optimization of Complex Systems in Nonstationary Environments
Authors: Cuie Yang, Jinliang Ding, Yaochu Jin, Tianyou Chai
Date: 2020-12-14 00:00:00
Tasks: Ensemble Learning, Outlier Detection

Existing work on data-driven optimization focuses on problems in static environments, but little attention has been paid to problems in dynamic environments. This paper proposes a data-driven optimization algorithm to deal with the challenges presented by the dynamic environments. First, a data stream ensemble learning method is adopted to train the surrogates so that each base learner of the ensemble learns the time-varying objective function in the previous environments. After that, a multi-task evolutionary algorithm is employed to simultaneously optimize the problems in the past environments assisted by the ensemble surrogate. This way, the optimization tasks in the previous environments can be used to accelerate the tracking of the optimum in the current environment. Since the real fitness function is not available for verifying the surrogates in offline data-driven optimization, a support vector domain description that was designed for outlier detection is introduced to select a reliable solution. Empirical results on six dynamic optimization benchmark problems demonstrate the effectiveness of the proposed algorithm compared with four state-of-the-art data-driven optimization algorithms.