pwc-archive/papers-with-abstracts huggingface dataset:
Exploration in Online Advertising Systems with Deep Uncertainty-Aware Learning
Authors: Chao Du, Zhifeng Gao, Shuo Yuan, Lining Gao, Ziyan Li, Yifan Zeng, Xiaoqiang Zhu, Jian Xu, Kun Gai, Kuang-Chih Lee
Date: 2020-11-25 00:00:00
Tasks: Click-Through Rate Prediction, Gaussian Processes

Modern online advertising systems inevitably rely on personalization methods, such as click-through rate (CTR) prediction. Recent progress in CTR prediction enjoys the rich representation capabilities of deep learning and achieves great success in large-scale industrial applications. However, these methods can suffer from lack of exploration. Another line of prior work addresses the exploration-exploitation trade-off problem with contextual bandit methods, which are recently less studied in the industry due to the difficulty in extending their flexibility with deep models. In this paper, we propose a novel Deep Uncertainty-Aware Learning (DUAL) method to learn CTR models based on Gaussian processes, which can provide predictive uncertainty estimations while maintaining the flexibility of deep neural networks. DUAL can be easily implemented on existing models and deployed in real-time systems with minimal extra computational overhead. By linking the predictive uncertainty estimation ability of DUAL to well-known bandit algorithms, we further present DUAL-based Ad-ranking strategies to boost up long-term utilities such as the social welfare in advertising systems. Experimental results on several public datasets demonstrate the effectiveness of our methods. Remarkably, an online A/B test deployed in the Alibaba display advertising platform shows an 8.2% social welfare improvement and an 8.0% revenue lift.
Detecting Video Game Player Burnout with the Use of Sensor Data and Machine Learning
Authors: Anton Smerdov, Andrey Somov, Evgeny Burnaev, Bo Zhou, Paul Lukowicz
Date: 2020-11-29 00:00:00
Tasks: BIG-bench Machine Learning, Interpretable Machine Learning, Multimodal Deep Learning, Person Re-Identification, Physiological Computing, Real-Time Strategy Games, Sensor Modeling, Skills Assessment, Skills Evaluation, Time Series Analysis

Current research in eSports lacks the tools for proper game practising and performance analytics. The majority of prior work relied only on in-game data for advising the players on how to perform better. However, in-game mechanics and trends are frequently changed by new patches limiting the lifespan of the models trained exclusively on the in-game logs. In this article, we propose the methods based on the sensor data analysis for predicting whether a player will win the future encounter. The sensor data were collected from 10 participants in 22 matches in League of Legends video game. We have trained machine learning models including Transformer and Gated Recurrent Unit to predict whether the player wins the encounter taking place after some fixed time in the future. For 10 seconds forecasting horizon Transformer neural network architecture achieves ROC AUC score 0.706. This model is further developed into the detector capable of predicting that a player will lose the encounter occurring in 10 seconds in 88.3% of cases with 73.5% accuracy. This might be used as a players' burnout or fatigue detector, advising players to retreat. We have also investigated which physiological features affect the chance to win or lose the next in-game encounter.
Linear Regression Evaluation of Search Engine Automatic Search Performance Based on Hadoop and R
Authors: Hong Xiong
Date: 2020-12-02 00:00:00
Tasks: Data Integration, regression, Retrieval

The automatic search performance of search engines has become an essential part of measuring the difference in user experience. An efficient automatic search system can significantly improve the performance of search engines and increase user traffic. Hadoop has strong data integration and analysis capabilities, while R has excellent statistical capabilities in linear regression. This article will propose a linear regression based on Hadoop and R to quantify the efficiency of the automatic retrieval system. We use R's functional properties to transform the user's search results upon linear correlations. In this way, the final output results have multiple display forms instead of web page preview interfaces. This article provides feasible solutions to the drawbacks of current search engine algorithms lacking once or twice search accuracies and multiple types of search results. We can conduct personalized regression analysis for user's needs with public datasets and optimize resources integration for most relevant information.
Coarse-to-Fine Entity Representations for Document-level Relation Extraction
Authors: Damai Dai, Jing Ren, Shuang Zeng, Baobao Chang, Zhifang Sui
Date: 2020-12-04 00:00:00
Tasks: Document-level Relation Extraction, Relation, Relation Extraction

Document-level Relation Extraction (RE) requires extracting relations expressed within and across sentences. Recent works show that graph-based methods, usually constructing a document-level graph that captures document-aware interactions, can obtain useful entity representations thus helping tackle document-level RE. These methods either focus more on the entire graph, or pay more attention to a part of the graph, e.g., paths between the target entity pair. However, we find that document-level RE may benefit from focusing on both of them simultaneously. Therefore, to obtain more comprehensive entity representations, we propose the Coarse-to-Fine Entity Representation model (CFER) that adopts a coarse-to-fine strategy involving two phases. First, CFER uses graph neural networks to integrate global information in the entire graph at a coarse level. Next, CFER utilizes the global information as a guidance to selectively aggregate path information between the target entity pair at a fine level. In classification, we combine the entity representations from both two levels into more comprehensive representations for relation extraction. Experimental results on two document-level RE datasets, DocRED and CDR, show that CFER outperforms existing models and is robust to the uneven label distribution.
To Schedule or not to Schedule: Extracting Task Specific Temporal Entities and Associated Negation Constraints
Authors: Barun Patra, Chala Fufa, Pamela Bhattacharya, Charles Lee
Date: 2020-11-15 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Negation, Scheduling

State of the art research for date-time entity extraction from text is task agnostic. Consequently, while the methods proposed in literature perform well for generic date-time extraction from texts, they don't fare as well on task specific date-time entity extraction where only a subset of the date-time entities present in the text are pertinent to solving the task. Furthermore, some tasks require identifying negation constraints associated with the date-time entities to correctly reason over time. We showcase a novel model for extracting task-specific date-time entities along with their negation constraints. We show the efficacy of our method on the task of date-time understanding in the context of scheduling meetings for an email-based digital AI scheduling assistant. Our method achieves an absolute gain of 19\% f-score points compared to baseline methods in detecting the date-time entities relevant to scheduling meetings and a 4\% improvement over baseline methods for detecting negation constraints over date-time entities.
Rethinking movie genre classification with fine-grained semantic clustering
Authors: Edward Fish, Jon Weinbren, Andrew Gilbert
Date: 2020-12-04 00:00:00
Tasks: Classification, Clustering, General Classification, Genre classification

Movie genre classification is an active research area in machine learning. However, due to the limited labels available, there can be large semantic variations between movies within a single genre definition. We expand these 'coarse' genre labels by identifying 'fine-grained' semantic information within the multi-modal content of movies. By leveraging pre-trained 'expert' networks, we learn the influence of different combinations of modes for multi-label genre classification. Using a contrastive loss, we continue to fine-tune this 'coarse' genre classification network to identify high-level intertextual similarities between the movies across all genre labels. This leads to a more 'fine-grained' and detailed clustering, based on semantic similarities while still retaining some genre information. Our approach is demonstrated on a newly introduced multi-modal 37,866,450 frame, 8,800 movie trailer dataset, MMX-Trailer-20, which includes pre-computed audio, location, motion, and image embeddings.
EventKG+TL: Creating Cross-Lingual Timelines from an Event-Centric Knowledge Graph
Authors: Simon Gottschalk, Elena Demidova
Date: 2018-05-03 00:00:00
Tasks: Knowledge Graphs

The provision of multilingual event-centric temporal knowledge graphs such as EventKG enables structured access to representations of a large number of historical and contemporary events in a variety of language contexts. Timelines provide an intuitive way to facilitate an overview of events related to a query entity - i.e., an entity or an event of user interest - over a certain period of time. In this paper, we present EventKG+TL - a novel system that generates cross-lingual event timelines using EventKG and facilitates an overview of the language-specific event relevance and popularity along with the cross-lingual differences.
Few-Shot Event Detection with Prototypical Amortized Conditional Random Field
Authors: Xin Cong, Shiyao Cui, Bowen Yu, Tingwen Liu, Yubin Wang, Bin Wang
Date: 2020-12-04 00:00:00
Proceeding: Findings (ACL) 2021 8
Tasks: Event Detection

Event detection tends to struggle when it needs to recognize novel event types with a few samples. The previous work attempts to solve this problem in the identify-then-classify manner but ignores the trigger discrepancy between event types, thus suffering from the error propagation. In this paper, we present a novel unified model which converts the task to a few-shot tagging problem with a double-part tagging scheme. To this end, we first propose the Prototypical Amortized Conditional Random Field (PA-CRF) to model the label dependency in the few-shot scenario, which approximates the transition scores between labels based on the label prototypes. Then Gaussian distribution is introduced for modeling of the transition scores to alleviate the uncertain estimation resulting from insufficient data. Experimental results show that the unified models work better than existing identify-then-classify models and our PA-CRF further achieves the best results on the benchmark dataset FewEvent. Our code and data are available at http://github.com/congxin95/PA-CRF.
Benchmarking Automated Clinical Language Simplification: Dataset, Algorithm, and Evaluation
Authors: Junyu Luo, Zifei Zheng, Hanzhong Ye, Muchao Ye, Yaqing Wang, Quanzeng You, Cao Xiao, Fenglong Ma
Date: 2020-12-04 00:00:00
Proceeding: COLING 2022 10
Tasks: Benchmarking, Machine Translation, Text Summarization, Translation

Patients with low health literacy usually have difficulty understanding medical jargon and the complex structure of professional medical language. Although some studies are proposed to automatically translate expert language into layperson-understandable language, only a few of them focus on both accuracy and readability aspects simultaneously in the clinical domain. Thus, simplification of the clinical language is still a challenging task, but unfortunately, it is not yet fully addressed in previous work. To benchmark this task, we construct a new dataset named MedLane to support the development and evaluation of automated clinical language simplification approaches. Besides, we propose a new model called DECLARE that follows the human annotation procedure and achieves state-of-the-art performance compared with eight strong baselines. To fairly evaluate the performance, we also propose three specific evaluation metrics. Experimental results demonstrate the utility of the annotated MedLane dataset and the effectiveness of the proposed model DECLARE.
Fine-tuning BERT for Low-Resource Natural Language Understanding via Active Learning
Authors: Daniel Grießhaber, Johannes Maucher, Ngoc Thang Vu
Date: 2020-12-04 00:00:00
Proceeding: COLING 2020 8
Tasks: Active Learning, Language Modeling, Language Modelling, Natural Language Understanding

Recently, leveraging pre-trained Transformer based language models in down stream, task specific models has advanced state of the art results in natural language understanding tasks. However, only a little research has explored the suitability of this approach in low resource settings with less than 1,000 training data points. In this work, we explore fine-tuning methods of BERT -- a pre-trained Transformer based language model -- by utilizing pool-based active learning to speed up training while keeping the cost of labeling new data constant. Our experimental results on the GLUE data set show an advantage in model performance by maximizing the approximate knowledge gain of the model when querying from the pool of unlabeled data. Finally, we demonstrate and analyze the benefits of freezing layers of the language model during fine-tuning to reduce the number of trainable parameters, making it more suitable for low-resource settings.
Data Processing and Annotation Schemes for FinCausal Shared Task
Authors: Dominique Mariko, Estelle Labidurie, Yagmur Ozturk, Hanna Abi Akl, Hugues de Mazancourt
Date: 2020-12-04 00:00:00

This document explains the annotation schemes used to label the data for the FinCausal Shared Task (Mariko et al., 2020). This task is associated to the Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation (FNP-FNS 2020), to be held at The 28th International Conference on Computational Linguistics (COLING'2020), on December 12, 2020.
CUED_speech at TREC 2020 Podcast Summarisation Track
Authors: Potsawee Manakul, Mark Gales
Date: 2020-12-04 00:00:00

In this paper, we describe our approach for the Podcast Summarisation challenge in TREC 2020. Given a podcast episode with its transcription, the goal is to generate a summary that captures the most important information in the content. Our approach consists of two steps: (1) Filtering redundant or less informative sentences in the transcription using the attention of a hierarchical model; (2) Applying a state-of-the-art text summarisation system (BART) fine-tuned on the Podcast data using a sequence-level reward function. Furthermore, we perform ensembles of three and nine models for our submission runs. We also fine-tune the BART model on the Podcast data as our baseline. The human evaluation by NIST shows that our best submission achieves 1.777 in the EGFB scale, while the score of creator-provided description is 1.291. Our system won the Spotify Podcast Summarisation Challenge in the TREC2020 Podcast Track in both human and automatic evaluation.
DDRel: A New Dataset for Interpersonal Relation Classification in Dyadic Dialogues
Authors: Qi Jia, Hongru Huang, Kenny Q. Zhu
Date: 2020-12-04 00:00:00
Tasks: Dialog Relation Extraction, General Classification, Relation, Relation Classification, Relation Extraction

Interpersonal language style shifting in dialogues is an interesting and almost instinctive ability of human. Understanding interpersonal relationship from language content is also a crucial step toward further understanding dialogues. Previous work mainly focuses on relation extraction between named entities in texts. In this paper, we propose the task of relation classification of interlocutors based on their dialogues. We crawled movie scripts from IMSDb, and annotated the relation labels for each session according to 13 pre-defined relationships. The annotated dataset DDRel consists of 6300 dyadic dialogue sessions between 694 pair of speakers with 53,126 utterances in total. We also construct session-level and pair-level relation classification tasks with widely-accepted baselines. The experimental results show that this task is challenging for existing models and the dataset will be useful for future research.
Automated Detection of Cyberbullying Against Women and Immigrants and Cross-domain Adaptability
Authors: Thushari Atapattu, Mahen Herath, Georgia Zhang, Katrina Falkner
Date: 2020-12-04 00:00:00
Proceeding: ALTA 2020 12

Cyberbullying is a prevalent and growing social problem due to the surge of social media technology usage. Minorities, women, and adolescents are among the common victims of cyberbullying. Despite the advancement of NLP technologies, the automated cyberbullying detection remains challenging. This paper focuses on advancing the technology using state-of-the-art NLP techniques. We use a Twitter dataset from SemEval 2019 - Task 5(HatEval) on hate speech against women and immigrants. Our best performing ensemble model based on DistilBERT has achieved 0.73 and 0.74 of F1 score in the task of classifying hate speech (Task A) and aggressiveness and target (Task B) respectively. We adapt the ensemble model developed for Task A to classify offensive language in external datasets and achieved ~0.7 of F1 score using three benchmark datasets, enabling promising results for cross-domain adaptability. We conduct a qualitative analysis of misclassified tweets to provide insightful recommendations for future cyberbullying research.
Ve'rdd. Narrowing the Gap between Paper Dictionaries, Low-Resource NLP and Community Involvement
Authors: Khalid Alnajjar, Mika Hämäläinen, Jack Rueter, Niko Partanen
Date: 2020-12-04 00:00:00
Proceeding: COLING 2020 8

We present an open-source online dictionary editing system, Ve'rdd, that offers a chance to re-evaluate and edit grassroots dictionaries that have been exposed to multiple amateur editors. The idea is to incorporate community activities into a state-of-the-art finite-state language description of a seriously endangered minority language, Skolt Sami. Problems involve getting the community to take part in things above the pencil-and-paper level. At times, it seems that the native speakers and the dictionary oriented are lacking technical understanding to utilize the infrastructures which might make their work more meaningful in the future, i.e. multiple reuse of all of their input. Therefore, our system integrates with the existing tools and infrastructures for Uralic language masking the technical complexities behind a user-friendly UI.
FinnSentiment -- A Finnish Social Media Corpus for Sentiment Polarity Annotation
Authors: Krister Lindén, Tommi Jauhiainen, Sam Hardwick
Date: 2020-12-04 00:00:00
Tasks: Opinion Mining, Sentence, Sentiment Analysis

Sentiment analysis and opinion mining is an important task with obvious application areas in social media, e.g. when indicating hate speech and fake news. In our survey of previous work, we note that there is no large-scale social media data set with sentiment polarity annotations for Finnish. This publications aims to remedy this shortcoming by introducing a 27,000 sentence data set annotated independently with sentiment polarity by three native annotators. We had the same three annotators for the whole data set, which provides a unique opportunity for further studies of annotator behaviour over time. We analyse their inter-annotator agreement and provide two baselines to validate the usefulness of the data set.
Event Guided Denoising for Multilingual Relation Learning
Authors: Amith Ananthram, Emily Allaway, Kathleen McKeown
Date: 2020-12-04 00:00:00
Tasks: Articles, Denoising, Relation, Relation Extraction

General purpose relation extraction has recently seen considerable gains in part due to a massively data-intensive distant supervision technique from Soares et al. (2019) that produces state-of-the-art results across many benchmarks. In this work, we present a methodology for collecting high quality training data for relation extraction from unlabeled text that achieves a near-recreation of their zero-shot and few-shot results at a fraction of the training cost. Our approach exploits the predictable distributional structure of date-marked news articles to build a denoised corpus -- the extraction process filters out low quality examples. We show that a smaller multilingual encoder trained on this corpus performs comparably to the current state-of-the-art (when both receive little to no fine-tuning) on few-shot and standard relation benchmarks in English and Spanish despite using many fewer examples (50k vs. 300mil+).
Addressing machine learning concept drift reveals declining vaccine sentiment during the COVID-19 pandemic
Authors: Martin Müller, Marcel Salathé
Date: 2020-12-03 00:00:00
Tasks: BIG-bench Machine Learning

Social media analysis has become a common approach to assess public opinion on various topics, including those about health, in near real-time. The growing volume of social media posts has led to an increased usage of modern machine learning methods in natural language processing. While the rapid dynamics of social media can capture underlying trends quickly, it also poses a technical problem: algorithms trained on annotated data in the past may underperform when applied to contemporary data. This phenomenon, known as concept drift, can be particularly problematic when rapid shifts occur either in the topic of interest itself, or in the way the topic is discussed. Here, we explore the effect of machine learning concept drift by focussing on vaccine sentiments expressed on Twitter, a topic of central importance especially during the COVID-19 pandemic. We show that while vaccine sentiment has declined considerably during the COVID-19 pandemic in 2020, algorithms trained on pre-pandemic data would have largely missed this decline due to concept drift. Our results suggest that social media analysis systems must address concept drift in a continuous fashion in order to avoid the risk of systematic misclassification of data, which is particularly likely during a crisis when the underlying data can change suddenly and rapidly.
A Correspondence Variational Autoencoder for Unsupervised Acoustic Word Embeddings
Authors: Puyuan Peng, Herman Kamper, Karen Livescu
Date: 2020-12-03 00:00:00
Tasks: Word Embeddings

We propose a new unsupervised model for mapping a variable-duration speech segment to a fixed-dimensional representation. The resulting acoustic word embeddings can form the basis of search, discovery, and indexing systems for low- and zero-resource languages. Our model, which we refer to as a maximal sampling correspondence variational autoencoder (MCVAE), is a recurrent neural network (RNN) trained with a novel self-supervised correspondence loss that encourages consistency between embeddings of different instances of the same word. Our training scheme improves on previous correspondence training approaches through the use and comparison of multiple samples from the approximate posterior distribution. In the zero-resource setting, the MCVAE can be trained in an unsupervised way, without any ground-truth word pairs, by using the word-like segments discovered via an unsupervised term discovery system. In both this setting and a semi-supervised low-resource setting (with a limited set of ground-truth word pairs), the MCVAE outperforms previous state-of-the-art models, such as Siamese-, CAE- and VAE-based RNNs.