Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

Yang Liu, Tim Paek, Manasi Patwardhan (Editors)

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New Orleans, Louisiana
Association for Computational Linguistics
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Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations
Yang Liu | Tim Paek | Manasi Patwardhan

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NLP Lean Programming Framework: Developing NLP Applications More Effectively
Marc Schreiber | Bodo Kraft | Albert Zündorf

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: Video Tutorial: (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

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Pay-Per-Request Deployment of Neural Network Models Using Serverless Architectures
Zhucheng Tu | Mengping Li | Jimmy Lin

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.

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An automated medical scribe for documenting clinical encounters
Gregory Finley | Erik Edwards | Amanda Robinson | Michael Brenndoerfer | Najmeh Sadoughi | James Fone | Nico Axtmann | Mark Miller | David Suendermann-Oeft

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.

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CL Scholar: The ACL Anthology Knowledge Graph Miner
Mayank Singh | Pradeep Dogga | Sohan Patro | Dhiraj Barnwal | Ritam Dutt | Rajarshi Haldar | Pawan Goyal | Animesh Mukherjee

We present CL Scholar, the ACL Anthology knowledge graph miner to facilitate high-quality search and exploration of current research progress in the computational linguistics community. In contrast to previous works, periodically crawling, indexing and processing of new incoming articles is completely automated in the current system. CL Scholar utilizes both textual and network information for knowledge graph construction. As an additional novel initiative, CL Scholar supports more than 1200 scholarly natural language queries along with standard keyword-based search on constructed knowledge graph. It answers binary, statistical and list based natural language queries. The current system is deployed at We also provide REST API support along with bulk download facility. Our code and data are available at

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ArgumenText: Searching for Arguments in Heterogeneous Sources
Christian Stab | Johannes Daxenberger | Chris Stahlhut | Tristan Miller | Benjamin Schiller | Christopher Tauchmann | Steffen Eger | Iryna Gurevych

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.

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ClaimRank: Detecting Check-Worthy Claims in Arabic and English
Israa Jaradat | Pepa Gencheva | Alberto Barrón-Cedeño | Lluís Màrquez | Preslav Nakov

We present ClaimRank, an online system for detecting check-worthy claims. While originally trained on political debates, the system can work for any kind of text, e.g., interviews or just regular news articles. Its aim is to facilitate manual fact-checking efforts by prioritizing the claims that fact-checkers should consider first. ClaimRank supports both Arabic and English, it is trained on actual annotations from nine reputable fact-checking organizations (PolitiFact, FactCheck, ABC, CNN, NPR, NYT, Chicago Tribune, The Guardian, and Washington Post), and thus it can mimic the claim selection strategies for each and any of them, as well as for the union of them all.

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360° Stance Detection
Sebastian Ruder | John Glover | Afshin Mehrabani | Parsa Ghaffari

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° 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.

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DebugSL: An Interactive Tool for Debugging Sentiment Lexicons
Andrew Schneider | John Male | Saroja Bhogadhi | Eduard Dragut

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 A screencast of DebugSL can be viewed at

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ELISA-EDL: A Cross-lingual Entity Extraction, Linking and Localization System
Boliang Zhang | Ying Lin | Xiaoman Pan | Di Lu | Jonathan May | Kevin Knight | Heng Ji

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.

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Entity Resolution and Location Disambiguation in the Ancient Hindu Temples Domain using Web Data
Ayush Maheshwari | Vishwajeet Kumar | Ganesh Ramakrishnan | J. Saketha Nath

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 Our system is open source and available on GitHub.

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Madly Ambiguous: A Game for Learning about Structural Ambiguity and Why It’s Hard for Computers
Ajda Gokcen | Ethan Hill | Michael White

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

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VnCoreNLP: A Vietnamese Natural Language Processing Toolkit
Thanh Vu | Dat Quoc Nguyen | Dai Quoc Nguyen | Mark Dras | Mark Johnson

We present an easy-to-use and fast toolkit, namely VnCoreNLP—a Java NLP annotation pipeline for Vietnamese. Our VnCoreNLP supports key natural language processing (NLP) tasks including word segmentation, part-of-speech (POS) tagging, named entity recognition (NER) and dependency parsing, and obtains state-of-the-art (SOTA) results for these tasks. We release VnCoreNLP to provide rich linguistic annotations to facilitate research work on Vietnamese NLP. Our VnCoreNLP is open-source and available at:

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CNNs for NLP in the Browser: Client-Side Deployment and Visualization Opportunities
Yiyun Liang | Zhucheng Tu | Laetitia Huang | Jimmy Lin

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.

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Generating Continuous Representations of Medical Texts
Graham Spinks | Marie-Francine Moens

We present an architecture that generates medical texts while learning an informative, continuous representation with discriminative features. During training the input to the system is a dataset of captions for medical X-Rays. The acquired continuous representations are of particular interest for use in many machine learning techniques where the discrete and high-dimensional nature of textual input is an obstacle. We use an Adversarially Regularized Autoencoder to create realistic text in both an unconditional and conditional setting. We show that this technique is applicable to medical texts which often contain syntactic and domain-specific shorthands. A quantitative evaluation shows that we achieve a lower model perplexity than a traditional LSTM generator.

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Vis-Eval Metric Viewer: A Visualisation Tool for Inspecting and Evaluating Metric Scores of Machine Translation Output
David Steele | Lucia Specia

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.

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Know Who Your Friends Are: Understanding Social Connections from Unstructured Text
Léa Deleris | Francesca Bonin | Elizabeth Daly | Stéphane Deparis | Yufang Hou | Charles Jochim | Yassine Lassoued | Killian Levacher

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.

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RiskFinder: A Sentence-level Risk Detector for Financial Reports
Yu-Wen Liu | Liang-Chih Liu | Chuan-Ju Wang | Ming-Feng Tsai

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

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SMILEE: Symmetric Multi-modal Interactions with Language-gesture Enabled (AI) Embodiment
Sujeong Kim | David Salter | Luke DeLuccia | Kilho Son | Mohamed R. Amer | Amir Tamrakar

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.

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Decision Conversations Decoded
Léa Deleris | Debasis Ganguly | Killian Levacher | Martin Stephenson | Francesca Bonin

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.

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Sounding Board: A User-Centric and Content-Driven Social Chatbot
Hao Fang | Hao Cheng | Maarten Sap | Elizabeth Clark | Ari Holtzman | Yejin Choi | Noah A. Smith | Mari Ostendorf

We present Sounding Board, a social chatbot that won the 2017 Amazon Alexa Prize. The system architecture consists of several components including spoken language processing, dialogue management, language generation, and content management, with emphasis on user-centric and content-driven design. We also share insights gained from large-scale online logs based on 160,000 conversations with real-world users.