pwc-archive/papers-with-abstracts huggingface dataset:
NeuralClassifier: An Open-source Neural Hierarchical Multi-label Text Classification Toolkit
Authors: Liqun Liu, Funan Mu, Pengyu Li, Xin Mu, Jing Tang, Xingsheng Ai, Ran Fu, LiFeng Wang, Xing Zhou
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Classification, General Classification, GPU, Hierarchical Multi-label Classification, Multi-class Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi Label Text Classification, Multi-Label Text Classification, text-classification, Text Classification

In this paper, we introduce NeuralClassifier, a toolkit for neural hierarchical multi-label text classification. NeuralClassifier is designed for quick implementation of neural models for hierarchical multi-label classification task, which is more challenging and common in real-world scenarios. A salient feature is that NeuralClassifier currently provides a variety of text encoders, such as FastText, TextCNN, TextRNN, RCNN, VDCNN, DPCNN, DRNN, AttentiveConvNet and Transformer encoder, etc. It also supports other text classification scenarios, including binary-class and multi-class classification. Built on PyTorch, the core operations are calculated in batch, making the toolkit efficient with the acceleration of GPU. Experiments show that models built in our toolkit achieve comparable performance with reported results in the literature.
ADVISER: A Dialog System Framework for Education \& Research
Authors: Daniel Ortega, Dirk V{\"a}th, Gianna Weber, V, Lindsey erlyn, Maximilian Schmidt, Moritz V{\"o}lkel, Zorica Karacevic, Ngoc Thang Vu
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7

In this paper, we present ADVISER - an open source dialog system framework for education and research purposes. This system supports multi-domain task-oriented conversations in two languages. It additionally provides a flexible architecture in which modules can be arbitrarily combined or exchanged - allowing for easy switching between rules-based and neural network based implementations. Furthermore, ADVISER offers a transparent, user-friendly framework designed for interdisciplinary collaboration: from a flexible back end, allowing easy integration of new features, to an intuitive graphical user interface supporting nontechnical users.
An Environment for Relational Annotation of Political Debates
Authors: Andre Blessing, Nico Blokker, Sebastian Haunss, Jonas Kuhn, Gabriella Lapesa, Sebastian Pad{\'o}
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: BIG-bench Machine Learning, Management

This paper describes the MARDY corpus annotation environment developed for a collaboration between political science and computational linguistics. The tool realizes the complete workflow necessary for annotating a large newspaper text collection with rich information about claims (demands) raised by politicians and other actors, including claim and actor spans, relations, and polarities. In addition to the annotation GUI, the tool supports the identification of relevant documents, text pre-processing, user management, integration of external knowledge bases, annotation comparison and merging, statistical analysis, and the incorporation of machine learning models as {``}pseudo-annotators{''}.
Microsoft Icecaps: An Open-Source Toolkit for Conversation Modeling
Authors: Vighnesh Leonardo Shiv, Chris Quirk, Anshuman Suri, Xiang Gao, Khuram Shahid, Nithya Govindarajan, Yizhe Zhang, Jianfeng Gao, Michel Galley, Chris Brockett, Tulasi Menon, Bill Dolan
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Language Modeling, Language Modelling, Response Generation

The Intelligent Conversation Engine: Code and Pre-trained Systems (Microsoft Icecaps) is an upcoming open-source natural language processing repository. Icecaps wraps TensorFlow functionality in a modular component-based architecture, presenting an intuitive and flexible paradigm for constructing sophisticated learning setups. Capabilities include multitask learning between models with shared parameters, upgraded language model decoding features, a range of built-in architectures, and a user-friendly data processing pipeline. The system is targeted toward conversational tasks, exploring diverse response generation, coherence, and knowledge grounding. Icecaps also provides pre-trained conversational models that can be either used directly or loaded for fine-tuning or bootstrapping other models; these models power an online demo of our framework.
My Turn To Read: An Interleaved E-book Reading Tool for Developing and Struggling Readers
Authors: Nitin Madnani, Beata Beigman Klebanov, Anastassia Loukina, Binod Gyawali, Patrick Lange, John Sabatini, Michael Flor
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7

Literacy is crucial for functioning in modern society. It underpins everything from educational attainment and employment opportunities to health outcomes. We describe My Turn To Read, an app that uses interleaved reading to help developing and struggling readers improve reading skills while reading for meaning and pleasure. We hypothesize that the longer-term impact of the app will be to help users become better, more confident readers with an increased stamina for extended reading. We describe the technology and present preliminary evidence in support of this hypothesis.
ClaimPortal: Integrated Monitoring, Searching, Checking, and Analytics of Factual Claims on Twitter
Authors: Sarthak Majithia, Fatma Arslan, Sumeet Lubal, Damian Jimenez, Priyank Arora, Josue Caraballo, Chengkai Li
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Fact Checking

We present ClaimPortal, a web-based platform for monitoring, searching, checking, and analyzing English factual claims on Twitter from the American political domain. We explain the architecture of ClaimPortal, its components and functions, and the user interface. While the last several years have witnessed a substantial growth in interests and efforts in the area of computational fact-checking, ClaimPortal is a novel infrastructure in that fact-checkers have largely skipped factual claims in tweets. It can be a highly powerful tool to both general web users and fact-checkers. It will also be an educational resource in helping cultivate a society that is less susceptible to falsehoods.
Flamb\'e: A Customizable Framework for Machine Learning Experiments
Authors: Jeremy Wohlwend, Nicholas Matthews, Ivan Itzcovich
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: BIG-bench Machine Learning, Language Modeling, Language Modelling, text-classification, Text Classification

Flamb{\'e} is a machine learning experimentation framework built to accelerate the entire research life cycle. Flamb{\'e}{'}s main objective is to provide a unified interface for prototyping models, running experiments containing complex pipelines, monitoring those experiments in real-time, reporting results, and deploying a final model for inference. Flamb{\'e} achieves both flexibility and simplicity by allowing users to write custom code but instantly include that code as a component in a larger system which is represented by a concise configuration file format. We demonstrate the application of the framework through a cutting-edge multistage use case: fine-tuning and distillation of a state of the art pretrained language model used for text classification.
A Modular Tool for Automatic Summarization
Authors: Valentin Nyzam, Aur{\'e}lien Bossard
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7

This paper introduces the first fine-grained modular tool for automatic summarization. Open source and written in Java, it is designed to be as straightforward as possible for end-users. Its modular architecture is meant to ease its maintenance and the development and integration of new modules. We hope that it will ease the work of researchers in automatic summarization by providing a reliable baseline for future works as well as an easy way to evaluate methods on different corpora.
TARGER: Neural Argument Mining at Your Fingertips
Authors: Artem Chernodub, Oleksiy Oliynyk, Philipp Heidenreich, Alex Bondarenko, Matthias Hagen, Chris Biemann, Alex Panchenko, er
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Argument Mining, Retrieval

We present TARGER, an open source neural argument mining framework for tagging arguments in free input texts and for keyword-based retrieval of arguments from an argument-tagged web-scale corpus. The currently available models are pre-trained on three recent argument mining datasets and enable the use of neural argument mining without any reproducibility effort on the user{'}s side. The open source code ensures portability to other domains and use cases.
MoNoise: A Multi-lingual and Easy-to-use Lexical Normalization Tool
Authors: Rob van der Goot
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Lexical Normalization

In this paper, we introduce and demonstrate the online demo as well as the command line interface of a lexical normalization system (MoNoise) for a variety of languages. We further improve this model by using features from the original word for every normalization candidate. For comparison with future work, we propose the bundling of seven datasets in six languages to form a new benchmark, together with a novel evaluation metric which is particularly suitable for cross-dataset comparisons. MoNoise reaches a new state-of-art performance for six out of seven of these datasets. Furthermore, we allow the user to tune the {`}aggressiveness{'} of the normalization, and show how the model can be made more efficient with only a small loss in performance. The online demo can be found on: http://www.robvandergoot.com/monoise and the corresponding code on: https://bitbucket.org/robvanderg/monoise/
Level-Up: Learning to Improve Proficiency Level of Essays
Authors: Wen-Bin Han, Jhih-Jie Chen, Ching-Yu Yang, Jason Chang
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Sentence

We introduce a method for generating suggestions on a given sentence for improving the proficiency level. In our approach, the sentence is transformed into a sequence of grammatical elements aimed at providing suggestions of more advanced grammar elements based on originals. The method involves parsing the sentence, identifying grammatical elements, and ranking related elements to recommend a higher level of grammatical element. We present a prototype tutoring system, Level-Up, that applies the method to English learners{'} essays in order to assist them in writing and reading. Evaluation on a set of essays shows that our method does assist user in writing.
Learning to Link Grammar and Encyclopedic Information of Assist ESL Learners
Authors: Jhih-Jie Chen, Ching-Yu Yang, Peichen Ho, Ming Chiao Tsai, Chia-Fang Ho, Kai-Wen Tuan, Chung-Ting Tsai, Wen-Bin Han, Jason Chang
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7

We introduce a system aimed at improving and expanding second language learners{'} English vocabulary. In addition to word definitions, we provide rich lexical information such as collocations and grammar patterns for target words. We present Linggle Booster that takes an article, identifies target vocabulary, provides lexical information, and generates a quiz on target words. Linggle Booster also links named-entity to corresponding Wikipedia pages. Evaluation on a set of target words shows that the method have reasonably good performance in terms of generating useful and information for learning vocabulary.
Extracting Kinship from Obituary to Enhance Electronic Health Records for Genetic Research
Authors: Kai He, Jialun Wu, Xiaoyong Ma, Chong Zhang, Ming Huang, Chen Li, Lixia Yao
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Relation Extraction

Claims database and electronic health records database do not usually capture kinship or family relationship information, which is imperative for genetic research. We identify online obituaries as a new data source and propose a special named entity recognition and relation extraction solution to extract names and kinships from online obituaries. Built on 1,809 annotated obituaries and a novel tagging scheme, our joint neural model achieved macro-averaged precision, recall and F measure of 72.69{\%}, 78.54{\%} and 74.93{\%}, and micro-averaged precision, recall and F measure of 95.74{\%}, 98.25{\%} and 96.98{\%} using 57 kinships with 10 or more examples in a 10-fold cross-validation experiment. The model performance improved dramatically when trained with 34 kinships with 50 or more examples. Leveraging additional information such as age, death date, birth date and residence mentioned by obituaries, we foresee a promising future of supplementing EHR databases with comprehensive and accurate kinship information for genetic research.
Lexical Normalization of User-Generated Medical Text
Authors: Anne Dirkson, Suzan Verberne, Wessel Kraaij
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Lexical Normalization, Mistake Detection, Spelling Correction

In the medical domain, user-generated social media text is increasingly used as a valuable complementary knowledge source to scientific medical literature. The extraction of this knowledge is complicated by colloquial language use and misspellings. Yet, lexical normalization of such data has not been addressed properly. This paper presents an unsupervised, data-driven spelling correction module for medical social media. Our method outperforms state-of-the-art spelling correction and can detect mistakes with an F0.5 of 0.888. Additionally, we present a novel corpus for spelling mistake detection and correction on a medical patient forum.
Overview of the Fourth Social Media Mining for Health (SMM4H) Shared Tasks at ACL 2019
Authors: Davy Weissenbacher, Abeed Sarker, Arjun Magge, Ashlynn Daughton, Karen O{'}Connor, Michael J. Paul, Gonzalez-Hern, Graciela ez
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Task 2

The number of users of social media continues to grow, with nearly half of adults worldwide and two-thirds of all American adults using social networking. Advances in automated data processing, machine learning and NLP present the possibility of utilizing this massive data source for biomedical and public health applications, if researchers address the methodological challenges unique to this media. We present the Social Media Mining for Health Shared Tasks collocated with the ACL at Florence in 2019, which address these challenges for health monitoring and surveillance, utilizing state of the art techniques for processing noisy, real-world, and substantially creative language expressions from social media users. For the fourth execution of this challenge, we proposed four different tasks. Task 1 asked participants to distinguish tweets reporting an adverse drug reaction (ADR) from those that do not. Task 2, a follow-up to Task 1, asked participants to identify the span of text in tweets reporting ADRs. Task 3 is an end-to-end task where the goal was to first detect tweets mentioning an ADR and then map the extracted colloquial mentions of ADRs in the tweets to their corresponding standard concept IDs in the MedDRA vocabulary. Finally, Task 4 asked participants to classify whether a tweet contains a personal mention of one{'}s health, a more general discussion of the health issue, or is an unrelated mention. A total of 34 teams from around the world registered and 19 teams from 12 countries submitted a system run. We summarize here the corpora for this challenge which are freely available at https://competitions.codalab.org/competitions/22521, and present an overview of the methods and the results of the competing systems.
MedNorm: A Corpus and Embeddings for Cross-terminology Medical Concept Normalisation
Authors: Maksim Belousov, William G. Dixon, Goran Nenadic
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Representation Learning

The medical concept normalisation task aims to map textual descriptions to standard terminologies such as SNOMED-CT or MedDRA. Existing publicly available datasets annotated using different terminologies cannot be simply merged and utilised, and therefore become less valuable when developing machine learning-based concept normalisation systems. To address that, we designed a data harmonisation pipeline and engineered a corpus of 27,979 textual descriptions simultaneously mapped to both MedDRA and SNOMED-CT, sourced from five publicly available datasets across biomedical and social media domains. The pipeline can be used in the future to integrate new datasets into the corpus and also could be applied in relevant data curation tasks. We also described a method to merge different terminologies into a single concept graph preserving their relations and demonstrated that representation learning approach based on random walks on a graph can efficiently encode both hierarchical and equivalent relations and capture semantic similarities not only between concepts inside a given terminology but also between concepts from different terminologies. We believe that making a corpus and embeddings for cross-terminology medical concept normalisation available to the research community would contribute to a better understanding of the task.
Passive Diagnosis Incorporating the PHQ-4 for Depression and Anxiety
Authors: Fionn Delahunty, Robert Johansson, Mihael Arcan
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

Depression and anxiety are the two most prevalent mental health disorders worldwide, impacting the lives of millions of people each year. In this work, we develop and evaluate a multilabel, multidimensional deep neural network designed to predict PHQ-4 scores based on individuals written text. Our system outperforms random baseline metrics and provides a novel approach to how we can predict psychometric scores from written text. Additionally, we explore how this architecture can be applied to analyse social media data.
HITSZ-ICRC: A Report for SMM4H Shared Task 2019-Automatic Classification and Extraction of Adverse Effect Mentions in Tweets
Authors: Shuai Chen, Yuanhang Huang, Xiaowei Huang, Haoming Qin, Jun Yan, Buzhou Tang
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

This is the system description of the Harbin Institute of Technology Shenzhen (HITSZ) team for the first and second subtasks of the fourth Social Media Mining for Health Applications (SMM4H) shared task in 2019. The two subtasks are automatic classification and extraction of adverse effect mentions in tweets. The systems for the two subtasks are based on bidirectional encoder representations from transformers (BERT), and achieves promising results. Among the systems we developed for subtask1, the best F1-score was 0.6457, for subtask2, the best relaxed F1-score and the best strict F1-score were 0.614 and 0.407 respectively. Our system ranks first among all systems on subtask1.
KFU NLP Team at SMM4H 2019 Tasks: Want to Extract Adverse Drugs Reactions from Tweets? BERT to The Rescue
Authors: Zulfat Miftahutdinov, Ilseyar Alimova, Elena Tutubalina
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Task 2

This paper describes a system developed for the Social Media Mining for Health (SMM4H) 2019 shared tasks. Specifically, we participated in three tasks. The goals of the first two tasks are to classify whether a tweet contains mentions of adverse drug reactions (ADR) and extract these mentions, respectively. The objective of the third task is to build an end-to-end solution: first, detect ADR mentions and then map these entities to concepts in a controlled vocabulary. We investigate the use of a language representation model BERT trained to obtain semantic representations of social media texts. Our experiments on a dataset of user reviews showed that BERT is superior to state-of-the-art models based on recurrent neural networks. The BERT-based system for Task 1 obtained an F1 of 57.38{\%}, with improvements up to +7.19{\%} F1 over a score averaged across all 43 submissions. The ensemble of neural networks with a voting scheme for named entity recognition ranked first among 9 teams at the SMM4H 2019 Task 2 and obtained a relaxed F1 of 65.8{\%}. The end-to-end model based on BERT for ADR normalization ranked first at the SMM4H 2019 Task 3 and obtained a relaxed F1 of 43.2{\%}.
Approaching SMM4H with Merged Models and Multi-task Learning
Authors: Tilia Ellendorff, Lenz Furrer, Nicola Colic, No{\"e}mi Aepli, Fabio Rinaldi
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Multi-Task Learning

We describe our submissions to the 4th edition of the Social Media Mining for Health Applications (SMM4H) shared task. Our team (UZH) participated in two sub-tasks: Automatic classifications of adverse effects mentions in tweets (Task 1) and Generalizable identification of personal health experience mentions (Task 4). For our submissions, we exploited ensembles based on a pre-trained language representation with a neural transformer architecture (BERT) (Tasks 1 and 4) and a CNN-BiLSTM(-CRF) network within a multi-task learning scenario (Task 1). These systems are placed on top of a carefully crafted pipeline of domain-specific preprocessing steps.
Identifying Adverse Drug Events Mentions in Tweets Using Attentive, Collocated, and Aggregated Medical Representation
Authors: Xinyan Zhao, Deahan Yu, V.G.Vinod Vydiswaran
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

Identifying mentions of medical concepts in social media is challenging because of high variability in free text. In this paper, we propose a novel neural network architecture, the Collocated LSTM with Attentive Pooling and Aggregated representation (CLAPA), that integrates a bidirectional LSTM model with attention and pooling strategy and utilizes the collocation information from training data to improve the representation of medical concepts. The collocation and aggregation layers improve the model performance on the task of identifying mentions of adverse drug events (ADE) in tweets. Using the dataset made available as part of the workshop shared task, we show that careful selection of neighborhood contexts can help uncover useful local information and improve the overall medical concept representation.
Transfer Learning for Health-related Twitter Data
Authors: Anne Dirkson, Suzan Verberne
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Transfer Learning

Transfer learning is promising for many NLP applications, especially in tasks with limited labeled data. This paper describes the methods developed by team TMRLeiden for the 2019 Social Media Mining for Health Applications (SMM4H) Shared Task. Our methods use state-of-the-art transfer learning methods to classify, extract and normalise adverse drug effects (ADRs) and to classify personal health mentions from health-related tweets. The code and fine-tuned models are publicly available.
NLP@UNED at SMM4H 2019: Neural Networks Applied to Automatic Classifications of Adverse Effects Mentions in Tweets
Authors: Javier Cortes-Tejada, Juan Martinez-Romo, Lourdes Araujo
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Word Embeddings

This paper describes a system for automatically classifying adverse effects mentions in tweets developed for the task 1 at Social Media Mining for Health Applications (SMM4H) Shared Task 2019. We have developed a system based on LSTM neural networks inspired by the excellent results obtained by deep learning classifiers in the last edition of this task. The network is trained along with Twitter GloVe pre-trained word embeddings.