Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)

Waleed Ammar, Annie Louis, Nasrin Mostafazadeh (Editors)

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Minneapolis, Minnesota
Association for Computational Linguistics
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Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)
Waleed Ammar | Annie Louis | Nasrin Mostafazadeh

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ADIDA : Automatic Dialect Identification for ArabicADIDA: Automatic Dialect Identification for Arabic
Ossama Obeid | Mohammad Salameh | Houda Bouamor | Nizar Habash

This demo paper describes ADIDA, a web-based system for automatic dialect identification for Arabic text. The system distinguishes among the dialects of 25 Arab cities (from Rabat to Muscat) in addition to Modern Standard Arabic. The results are presented with either a point map or a heat map visualizing the automatic identification probabilities over a geographical map of the Arab World.

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INS : An Interactive Chinese News Synthesis SystemINS: An Interactive Chinese News Synthesis System
Hui Liu | Wentao Qin | Xiaojun Wan

Nowadays, we are surrounded by more and more online news articles. Tens or hundreds of news articles need to be read if we wish to explore a hot news event or topic. So it is of vital importance to automatically synthesize a batch of news articles related to the event or topic into a new synthesis article (or overview article) for reader’s convenience. It is so challenging to make news synthesis fully automatic that there is no successful solution by now. In this paper, we put forward a novel Interactive News Synthesis system (i.e. INS), which can help generate news overview articles automatically or by interacting with users. More importantly, INS can serve as a tool for editors to help them finish their jobs. In our experiments, INS performs well on both topic representation and synthesis article generation. A user study also demonstrates the usefulness and users’ satisfaction with the INS tool. A demo video is available at.

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Train, Sort, Explain : Learning to Diagnose Translation Models
Robert Schwarzenberg | David Harbecke | Vivien Macketanz | Eleftherios Avramidis | Sebastian Möller

Evaluating translation models is a trade-off between effort and detail. On the one end of the spectrum there are automatic count-based methods such as BLEU, on the other end linguistic evaluations by humans, which arguably are more informative but also require a disproportionately high effort. To narrow the spectrum, we propose a general approach on how to automatically expose systematic differences between human and machine translations to human experts. Inspired by adversarial settings, we train a neural text classifier to distinguish human from machine translations. A classifier that performs and generalizes well after training should recognize systematic differences between the two classes, which we uncover with neural explainability methods. Our proof-of-concept implementation, DiaMaT, is open source. Applied to a dataset translated by a state-of-the-art neural Transformer model, DiaMaT achieves a classification accuracy of 75 % and exposes meaningful differences between humans and the Transformer, amidst the current discussion about human parity.

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LeafNATS : An Open-Source Toolkit and Live Demo System for Neural Abstractive Text SummarizationLeafNATS: An Open-Source Toolkit and Live Demo System for Neural Abstractive Text Summarization
Tian Shi | Ping Wang | Chandan K. Reddy

Neural abstractive text summarization (NATS) has received a lot of attention in the past few years from both industry and academia. In this paper, we introduce an open-source toolkit, namely LeafNATS, for training and evaluation of different sequence-to-sequence based models for the NATS task, and for deploying the pre-trained models to real-world applications. The toolkit is modularized and extensible in addition to maintaining competitive performance in the NATS task. A live news blogging system has also been implemented to demonstrate how these models can aid blog / news editors by providing them suggestions of headlines and summaries of their articles.

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FAKTA : An Automatic End-to-End Fact Checking SystemFAKTA: An Automatic End-to-End Fact Checking System
Moin Nadeem | Wei Fang | Brian Xu | Mitra Mohtarami | James Glass

We present FAKTA which is a unified framework that integrates various components of a fact-checking process : document retrieval from media sources with various types of reliability, stance detection of documents with respect to given claims, evidence extraction, and linguistic analysis. FAKTA predicts the factuality of given claims and provides evidence at the document and sentence level to explain its predictions.

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Plan, Write, and Revise : an Interactive System for Open-Domain Story Generation
Seraphina Goldfarb-Tarrant | Haining Feng | Nanyun Peng

Story composition is a challenging problem for machines and even for humans. We present a neural narrative generation system that interacts with humans to generate stories. Our system has different levels of human interaction, which enables us to understand at what stage of story-writing human collaboration is most productive, both to improving story quality and human engagement in the writing process. We compare different varieties of interaction in story-writing, story-planning, and diversity controls under time constraints, and show that increased types of human collaboration at both planning and writing stages results in a 10-50 % improvement in story quality as compared to less interactive baselines. We also show an accompanying increase in user engagement and satisfaction with stories as compared to our own less interactive systems and to previous turn-taking approaches to interaction. Finally, we find that humans tasked with collaboratively improving a particular characteristic of a story are in fact able to do so, which has implications for future uses of human-in-the-loop systems.

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LT Expertfinder : An Evaluation Framework for Expert Finding MethodsLT Expertfinder: An Evaluation Framework for Expert Finding Methods
Tim Fischer | Steffen Remus | Chris Biemann

Expert finding is the task of ranking persons for a predefined topic or search query. Finding experts for a specified area is an important task and has attracted much attention in the information retrieval community. Most approaches for this task are evaluated in a supervised fashion, which depend on predefined topics of interest as well as gold standard expert rankings. Famous representatives of such datasets are enriched versions of DBLP provided by the ArnetMiner projet or the W3C Corpus of TREC. However, manually ranking experts can be considered highly subjective and detailed rankings are hardly distinguishable. Evaluating these datasets does not necessarily guarantee a good or bad performance of the system. Particularly for dynamic systems, where topics are not predefined but formulated as a search query, we believe a more informative approach is to perform user studies for directly comparing different methods in the same view. In order to accomplish this in a user-friendly way, we present the LT Expert Finder web-application, which is equipped with various query-based expert finding methods that can be easily extended, a detailed expert profile view, detailed evidence in form of relevant documents and statistics, and an evaluation component that allows the qualitative comparison between different rankings.

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Litigation Analytics : Extracting and querying motions and orders from US federal courtsUS federal courts
Thomas Vacek | Dezhao Song | Hugo Molina-Salgado | Ronald Teo | Conner Cowling | Frank Schilder

Legal litigation planning can benefit from statistics collected from past decisions made by judges. Information on the typical duration for a submitted motion, for example, can give valuable clues for developing a successful strategy. Such information is encoded in semi-structured documents called dockets. In order to extract and aggregate this information, we deployed various information extraction and machine learning techniques. The aggregated data can be queried in real time within the Westlaw Edge search engine. In addition to a keyword search for judges, lawyers, law firms, parties and courts, we also implemented a question answering interface that offers targeted questions in order to get to the respective answers quicker.

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A Research Platform for Multi-Robot Dialogue with HumansResearch Platform for Multi-Robot Dialogue with Humans
Matthew Marge | Stephen Nogar | Cory J. Hayes | Stephanie M. Lukin | Jesse Bloecker | Eric Holder | Clare Voss

This paper presents a research platform that supports spoken dialogue interaction with multiple robots. The demonstration showcases our crafted MultiBot testing scenario in which users can verbally issue search, navigate, and follow instructions to two robotic teammates : a simulated ground robot and an aerial robot. This flexible language and robotic platform takes advantage of existing tools for speech recognition and dialogue management that are compatible with new domains, and implements an inter-agent communication protocol (tactical behavior specification), where verbal instructions are encoded for tasks assigned to the appropriate robot.

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Chat-crowd : A Dialog-based Platform for Visual Layout Composition
Paola Cascante-Bonilla | Xuwang Yin | Vicente Ordonez | Song Feng

In this paper we introduce Chat-crowd, an interactive environment for visual layout composition via conversational interactions. Chat-crowd supports multiple agents with two conversational roles : agents who play the role of a designer are in charge of placing objects in an editable canvas according to instructions or commands issued by agents with a director role. The system can be integrated with crowdsourcing platforms for both synchronous and asynchronous data collection and is equipped with comprehensive quality controls on the performance of both types of agents. We expect that this system will be useful to build multimodal goal-oriented dialog tasks that require spatial and geometric reasoning.