pwc-archive/papers-with-abstracts huggingface dataset:
Using Classifier Features to Determine Language Transfer on Morphemes
Authors: Alex Lavrentovich, ra
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Cross-corpus, Language Acquisition, Language Identification, Native Language Identification, Text Classification

The aim of this thesis is to perform a Native Language Identification (NLI) task where we identify an English learner{'}s native language background based only on the learner{'}s English writing samples. We focus on the use of English grammatical morphemes across four proficiency levels. The outcome of the computational task is connected to a position in second language acquisition research that holds all learners acquire English grammatical morphemes in the same order, regardless of native language background. We use the NLI task as a tool to uncover cross-linguistic influence on the developmental trajectory of morphemes. We perform a cross-corpus evaluation across proficiency levels to increase the reliability and validity of the linguistic features that predict the native language background. We include native English data to determine the different morpheme patterns used by native versus non-native English speakers. Furthermore, we conduct a human NLI task to determine the type and magnitude of language transfer cues used by human raters versus the classifier.
End-to-End Learning of Task-Oriented Dialogs
Authors: Bing Liu, Ian Lane
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Multi-Task Learning, Reinforcement Learning, Spoken Language Understanding

In this thesis proposal, we address the limitations of conventional pipeline design of task-oriented dialog systems and propose end-to-end learning solutions. We design neural network based dialog system that is able to robustly track dialog state, interface with knowledge bases, and incorporate structured query results into system responses to successfully complete task-oriented dialog. In learning such neural network based dialog systems, we propose hybrid offline training and online interactive learning methods. We introduce a multi-task learning method in pre-training the dialog agent in a supervised manner using task-oriented dialog corpora. The supervised training agent can further be improved via interacting with users and learning online from user demonstration and feedback with imitation and reinforcement learning. In addressing the sample efficiency issue with online policy learning, we further propose a method by combining the learning-from-user and learning-from-simulation approaches to improve the online interactive learning efficiency.
Towards Generating Personalized Hospitalization Summaries
Authors: Sabita Acharya, Barbara Di Eugenio, Andrew Boyd, Richard Cameron, Karen Dunn Lopez, Pamela Martyn-Nemeth, Carolyn Dickens, Amer Ardati
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Document Summarization, Multi-Document Summarization, Text Generation

Most of the health documents, including patient education materials and discharge notes, are usually flooded with medical jargons and contain a lot of generic information about the health issue. In addition, patients are only provided with the doctor{'}s perspective of what happened to them in the hospital while the care procedure performed by nurses during their entire hospital stay is nowhere included. The main focus of this research is to generate personalized hospital-stay summaries for patients by combining information from physician discharge notes and nursing plan of care. It uses a metric to identify medical concepts that are Complex, extracts definitions for the concept from three external knowledge sources, and provides the simplest definition to the patient. It also takes various features of the patient into account, like their concerns and strengths, ability to understand basic health information, level of engagement in taking care of their health, and familiarity with the health issue and personalizes the content of the summaries accordingly. Our evaluation showed that the summaries contain 80{\%} of the medical concepts that are considered as being important by both doctor and nurses. Three patient advisors (i.e. individuals who are trained in understanding patient experience extensively) verified the usability of our summaries and mentioned that they would like to get such summaries when they are discharged from hospital.
Read and Comprehend by Gated-Attention Reader with More Belief
Authors: Haohui Deng, Yik-Cheung Tam
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Position, Reading Comprehension, Word Alignment

Gated-Attention (GA) Reader has been effective for reading comprehension. GA Reader makes two assumptions: (1) a uni-directional attention that uses an input query to gate token encodings of a document; (2) encoding at the cloze position of an input query is considered for answer prediction. In this paper, we propose Collaborative Gating (CG) and Self-Belief Aggregation (SBA) to address the above assumptions respectively. In CG, we first use an input document to gate token encodings of an input query so that the influence of irrelevant query tokens may be reduced. Then the filtered query is used to gate token encodings of an document in a collaborative fashion. In SBA, we conjecture that query tokens other than the cloze token may be informative for answer prediction. We apply self-attention to link the cloze token with other tokens in a query so that the importance of query tokens with respect to the cloze position are weighted. Then their evidences are weighted, propagated and aggregated for better reading comprehension. Experiments show that our approaches advance the state-of-theart results in CNN, Daily Mail, and Who Did What public test sets.
Corpus Creation and Emotion Prediction for Hindi-English Code-Mixed Social Media Text
Authors: Deepanshu Vijay, Aditya Bohra, Vinay Singh, Syed Sarfaraz Akhtar, Manish Shrivastava
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: General Classification

Emotion Prediction is a Natural Language Processing (NLP) task dealing with detection and classification of emotions in various monolingual and bilingual texts. While some work has been done on code-mixed social media text and in emotion prediction separately, our work is the first attempt which aims at identifying the emotion associated with Hindi-English code-mixed social media text. In this paper, we analyze the problem of emotion identification in code-mixed content and present a Hindi-English code-mixed corpus extracted from twitter and annotated with the associated emotion. For every tweet in the dataset, we annotate the source language of all the words present, and also the causal language of the expressed emotion. Finally, we propose a supervised classification system which uses various machine learning techniques for detecting the emotion associated with the text using a variety of character level, word level, and lexicon based features.
Sensing and Learning Human Annotators Engaged in Narrative Sensemaking
Authors: McKenna Tornblad, Luke Lapresi, Christopher Homan, Raymond Ptucha, Cecilia Ovesdotter Alm
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6

While labor issues and quality assurance in crowdwork are increasingly studied, how annotators make sense of texts and how they are personally impacted by doing so are not. We study these questions via a narrative-sorting annotation task, where carefully selected (by sequentiality, topic, emotional content, and length) collections of tweets serve as examples of everyday storytelling. As readers process these narratives, we measure their facial expressions, galvanic skin response, and self-reported reactions. From the perspective of annotator well-being, a reassuring outcome was that the sorting task did not cause a measurable stress response, however readers reacted to humor. In terms of sensemaking, readers were more confident when sorting sequential, target-topical, and highly emotional tweets. As crowdsourcing becomes more common, this research sheds light onto the perceptive capabilities and emotional impact of human readers.
Generating Image Captions in Arabic using Root-Word Based Recurrent Neural Networks and Deep Neural Networks
Authors: Vasu Jindal
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Caption Generation, Image Captioning, Image Classification, Image Description, Information Retrieval

Image caption generation has gathered widespread interest in the artificial intelligence community. Automatic generation of an image description requires both computer vision and natural language processing techniques. While, there has been advanced research in the English caption generation, research on generating Arabic descriptions of an image is extremely limited. Semitic languages like Arabic are heavily influenced by root-words. We leverage this critical dependency of Arabic to generate captions of an image directly in Arabic using root-word based Recurrent Neural Network and Deep Neural Networks. Experimental results on dataset from various Middle Eastern newspaper websites allow us to report the first BLEU score for direct Arabic caption generation. We also compare the results of our approach with BLEU score captions generated in English and translated in Arabic. Experimental results confirm that generating image captions using root-words directly in Arabic significantly outperforms the English-Arabic translated captions using state-of-the-art methods.
NLP Lean Programming Framework: Developing NLP Applications More Effectively
Authors: Marc Schreiber, Bodo Kraft, Albert Z{\"u}ndorf
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Named Entity Recognition (NER)

This paper presents NLP Lean Programming framework (NLPf), a new framework for creating custom Natural Language Processing (NLP) models and pipelines by utilizing common software development build systems. This approach allows developers to train and integrate domain-specific NLP pipelines into their applications seamlessly. Additionally, NLPf provides an annotation tool which improves the annotation process significantly by providing a well-designed GUI and sophisticated way of using input devices. Due to NLPf{'}s properties developers and domain experts are able to build domain-specific NLP application more effectively. Project page: \url{https://gitlab.com/schrieveslaach/NLPf} Video Tutorial: \url{https://www.youtube.com/watch?v=44UJspVebTA} (Demonstration starts at 11:40 min) This paper is related to: - Interfaces and resources to support linguistic annotation - Software architectures and reusable components - Software tools for evaluation or error analysis
Pay-Per-Request Deployment of Neural Network Models Using Serverless Architectures
Authors: Zhucheng Tu, Mengping Li, Jimmy Lin
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Answer Selection, Management, Question Answering, Sentence Classification, Word Embeddings

We demonstrate the serverless deployment of neural networks for model inferencing in NLP applications using Amazon{'}s Lambda service for feedforward evaluation and DynamoDB for storing word embeddings. Our architecture realizes a pay-per-request pricing model, requiring zero ongoing costs for maintaining server instances. All virtual machine management is handled behind the scenes by the cloud provider without any direct developer intervention. We describe a number of techniques that allow efficient use of serverless resources, and evaluations confirm that our design is both scalable and inexpensive.
An automated medical scribe for documenting clinical encounters
Authors: Gregory Finley, Erik Edwards, Am Robinson, a, Michael Brenndoerfer, Najmeh Sadoughi, James Fone, Nico Axtmann, Mark Miller, David Suendermann-Oeft
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: speaker-diarization, Speaker Diarization, speech-recognition, Speech Recognition, Text Generation

A medical scribe is a clinical professional who charts patient{--}physician encounters in real time, relieving physicians of most of their administrative burden and substantially increasing productivity and job satisfaction. We present a complete implementation of an automated medical scribe. Our system can serve either as a scalable, standardized, and economical alternative to human scribes; or as an assistive tool for them, providing a first draft of a report along with a convenient means to modify it. This solution is, to our knowledge, the first automated scribe ever presented and relies upon multiple speech and language technologies, including speaker diarization, medical speech recognition, knowledge extraction, and natural language generation.
ArgumenText: Searching for Arguments in Heterogeneous Sources
Authors: Christian Stab, Johannes Daxenberger, Chris Stahlhut, Tristan Miller, Benjamin Schiller, Christopher Tauchmann, Steffen Eger, Iryna Gurevych
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Argument Mining, Argument Retrieval, Information Retrieval, Question Answering, Retrieval, valid

Argument mining is a core technology for enabling argument search in large corpora. However, most current approaches fall short when applied to heterogeneous texts. In this paper, we present an argument retrieval system capable of retrieving sentential arguments for any given controversial topic. By analyzing the highest-ranked results extracted from Web sources, we found that our system covers 89{\%} of arguments found in expert-curated lists of arguments from an online debate portal, and also identifies additional valid arguments.
360\mbox$^\circ$ Stance Detection
Authors: Sebastian Ruder, John Glover, Afshin Mehrabani, Parsa Ghaffari
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Stance Detection

The proliferation of fake news and filter bubbles makes it increasingly difficult to form an unbiased, balanced opinion towards a topic. To ameliorate this, we propose 360{\mbox{$^\circ$}} Stance Detection, a tool that aggregates news with multiple perspectives on a topic. It presents them on a spectrum ranging from support to opposition, enabling the user to base their opinion on multiple pieces of diverse evidence.
DebugSL: An Interactive Tool for Debugging Sentiment Lexicons
Authors: Andrew Schneider, John Male, Saroja Bhogadhi, Eduard Dragut
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Sentiment Analysis

We introduce DebugSL, a visual (Web) debugging tool for sentiment lexicons (SLs). Its core component implements our algorithms for the automatic detection of polarity inconsistencies in SLs. An inconsistency is a set of words and/or word-senses whose polarity assignments cannot all be simultaneously satisfied. DebugSL finds inconsistencies of small sizes in SLs and has a rich user interface which helps users in the correction process. The project source code is available at \url{https://github.com/atschneid/DebugSL} A screencast of DebugSL can be viewed at \url{https://cis.temple.edu/~edragut/DebugSL.webm}
ELISA-EDL: A Cross-lingual Entity Extraction, Linking and Localization System
Authors: Boliang Zhang, Ying Lin, Xiaoman Pan, Di Lu, Jonathan May, Kevin Knight, Heng Ji
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Entity Extraction using GAN, Entity Linking, Transfer Learning

We demonstrate ELISA-EDL, a state-of-the-art re-trainable system to extract entity mentions from low-resource languages, link them to external English knowledge bases, and visualize locations related to disaster topics on a world heatmap. We make all of our data sets, resources and system training and testing APIs publicly available for research purpose.
Entity Resolution and Location Disambiguation in the Ancient Hindu Temples Domain using Web Data
Authors: Ayush Maheshwari, Vishwajeet Kumar, Ganesh Ramakrishnan, J. Saketha Nath
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Clustering, Entity Resolution

We present a system for resolving entities and disambiguating locations based on publicly available web data in the domain of ancient Hindu Temples. Scarce, unstructured information poses a challenge to Entity Resolution(ER) and snippet ranking. Additionally, because the same set of entities may be associated with multiple locations, Location Disambiguation(LD) is a problem. The mentions and descriptions of temples exist in the order of hundreds of thousands, with such data generated by various users in various forms such as text (Wikipedia pages), videos (YouTube videos), blogs, etc. We demonstrate an integrated approach using a combination of grammar rules for parsing and unsupervised (clustering) algorithms to resolve entity and locations with high confidence. A demo of our system is accessible at \url{tinyurl.com/templedemos}. Our system is open source and available on GitHub.
Madly Ambiguous: A Game for Learning about Structural Ambiguity and Why It's Hard for Computers
Authors: Ajda Gokcen, Ethan Hill, Michael White
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Prepositional Phrase Attachment, Sentence, Text Generation

Madly Ambiguous is an open source, online game aimed at teaching audiences of all ages about structural ambiguity and why it{'}s hard for computers. After a brief introduction to structural ambiguity, users are challenged to complete a sentence in a way that tricks the computer into guessing an incorrect interpretation. Behind the scenes are two different NLP-based methods for classifying the user{'}s input, one representative of classic rule-based approaches to disambiguation and the other representative of recent neural network approaches. Qualitative feedback from the system{'}s use in online, classroom, and science museum settings indicates that it is engaging and successful in conveying the intended take home messages. A demo of Madly Ambiguous can be played at \url{http://madlyambiguous.osu.edu}.
CNNs for NLP in the Browser: Client-Side Deployment and Visualization Opportunities
Authors: Yiyun Liang, Zhucheng Tu, Laetitia Huang, Jimmy Lin
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Interpretable Machine Learning, Sentence Classification, Sentiment Analysis

We demonstrate a JavaScript implementation of a convolutional neural network that performs feedforward inference completely in the browser. Such a deployment means that models can run completely on the client, on a wide range of devices, without making backend server requests. This design is useful for applications with stringent latency requirements or low connectivity. Our evaluations show the feasibility of JavaScript as a deployment target. Furthermore, an in-browser implementation enables seamless integration with the JavaScript ecosystem for information visualization, providing opportunities to visually inspect neural networks and better understand their inner workings.
Vis-Eval Metric Viewer: A Visualisation Tool for Inspecting and Evaluating Metric Scores of Machine Translation Output
Authors: David Steele, Lucia Specia
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Machine Translation, Sentence, Translation

Machine Translation systems are usually evaluated and compared using automated evaluation metrics such as BLEU and METEOR to score the generated translations against human translations. However, the interaction with the output from the metrics is relatively limited and results are commonly a single score along with a few additional statistics. Whilst this may be enough for system comparison it does not provide much useful feedback or a means for inspecting translations and their respective scores. VisEval Metric Viewer VEMV is a tool designed to provide visualisation of multiple evaluation scores so they can be easily interpreted by a user. VEMV takes in the source, reference, and hypothesis files as parameters, and scores the hypotheses using several popular evaluation metrics simultaneously. Scores are produced at both the sentence and dataset level and results are written locally to a series of HTML files that can be viewed on a web browser. The individual scored sentences can easily be inspected using powerful search and selection functions and results can be visualised with graphical representations of the scores and distributions.
Know Who Your Friends Are: Understanding Social Connections from Unstructured Text
Authors: L{\'e}a Deleris, Francesca Bonin, Elizabeth Daly, St{\'e}phane Deparis, Yufang Hou, Charles Jochim, Yassine Lassoued, Killian Levacher
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6

Having an understanding of interpersonal relationships is helpful in many contexts. Our system seeks to assist humans with that task, using textual information (e.g., case notes, speech transcripts, posts, books) as input. Specifically, our system first extracts qualitative and quantitative information elements (which we call signals) about interactions among persons, aggregates those to provide a condensed view of relationships and then enables users to explore all facets of the resulting social (multi-)graph through a visual interface.
RiskFinder: A Sentence-level Risk Detector for Financial Reports
Authors: Yu-Wen Liu, Liang-Chih Liu, Chuan-Ju Wang, Ming-Feng Tsai
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Sentence, Sentence Embedding, Sentence-Embedding, Sentence Embeddings, Sentiment Analysis, Time Series, Time Series Analysis

This paper presents a web-based information system, RiskFinder, for facilitating the analyses of soft and hard information in financial reports. In particular, the system broadens the analyses from the word level to sentence level, which makes the system useful for practitioner communities and unprecedented among financial academics. The proposed system has four main components: 1) a Form 10-K risk-sentiment dataset, consisting of a set of risk-labeled financial sentences and pre-trained sentence embeddings; 2) metadata, including basic information on each company that published the Form 10-K financial report as well as several relevant financial measures; 3) an interface that highlights risk-related sentences in the financial reports based on the latest sentence embedding techniques; 4) a visualization of financial time-series data for a corresponding company. This paper also conducts some case studies to showcase that the system can be of great help in capturing valuable insight within large amounts of textual information. The system is now online available at \url{https://cfda.csie.org/RiskFinder/}.
SMILEE: Symmetric Multi-modal Interactions with Language-gesture Enabled (AI) Embodiment
Authors: Sujeong Kim, David Salter, Luke DeLuccia, Kilho Son, Mohamed R. Amer, Amir Tamrakar
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Speech Recognition

We demonstrate an intelligent conversational agent system designed for advancing human-machine collaborative tasks. The agent is able to interpret a user{'}s communicative intent from both their verbal utterances and non-verbal behaviors, such as gestures. The agent is also itself able to communicate both with natural language and gestures, through its embodiment as an avatar thus facilitating natural symmetric multi-modal interactions. We demonstrate two intelligent agents with specialized skills in the Blocks World as use-cases of our system.
Decision Conversations Decoded
Authors: L{\'e}a Deleris, Debasis Ganguly, Killian Levacher, Martin Stephenson, Francesca Bonin
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Decision Making

We describe the vision and current version of a Natural Language Processing system aimed at group decision making facilitation. Borrowing from the scientific field of Decision Analysis, its essential role is to identify alternatives and criteria associated with a given decision, to keep track of who proposed them and of the expressed sentiment towards them. Based on this information, the system can help identify agreement and dissent or recommend an alternative. Overall, it seeks to help a group reach a decision in a natural yet auditable fashion.