@Book{EMNLP2017Demos:2017,
  editor    = {Lucia Specia  and  Matt Post  and  Michael Paul},
  title     = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  url       = {http://www.aclweb.org/anthology/D17-2}
}

@InProceedings{schneider-wooters:2017:EMNLP2017Demos,
  author    = {Schneider, Nathan  and  Wooters, Chuck},
  title     = {The NLTK FrameNet API: Designing for Discoverability with a Rich Linguistic Resource},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {1--6},
  abstract  = {A new Python API, integrated within the NLTK suite, offers access to the
	FrameNet 1.7 lexical database. The lexicon (structured in terms of frames) as
	well as annotated sentences can be processed programatically, or browsed with
	human-readable displays via the interactive Python prompt.},
  url       = {http://www.aclweb.org/anthology/D17-2001}
}

@InProceedings{habernal-EtAl:2017:EMNLP2017Demos,
  author    = {Habernal, Ivan  and  Hannemann, Raffael  and  Pollak, Christian  and  Klamm, Christopher  and  Pauli, Patrick  and  Gurevych, Iryna},
  title     = {Argotario: Computational Argumentation Meets Serious Games},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {7--12},
  abstract  = {An important skill in critical thinking and argumentation is the ability to
	spot and recognize fallacies. Fallacious arguments, omnipresent in
	argumentative discourse, can be deceptive, manipulative, or simply leading to
	'wrong moves' in a discussion. Despite their importance, argumentation scholars
	and NLP researchers with focus on argumentation quality have not yet
	investigated fallacies empirically. The nonexistence of resources dealing with
	fallacious argumentation calls for scalable approaches to data acquisition and
	annotation, for which the serious games methodology offers an appealing, yet
	unexplored, alternative. We present Argotario, a serious game that deals with
	fallacies in everyday argumentation. Argotario is a multilingual, open-source,
	platform-independent application with strong educational aspects, accessible at
	www.argotario.net.},
  url       = {http://www.aclweb.org/anthology/D17-2002}
}

@InProceedings{ovesdotteralm-meyers-prudhommeaux:2017:EMNLP2017Demos,
  author    = {Ovesdotter Alm, Cecilia  and  Meyers, Benjamin  and  Prud'hommeaux, Emily},
  title     = {An Analysis and Visualization Tool for Case Study Learning of Linguistic Concepts},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {13--18},
  abstract  = {We present an educational tool that integrates computational linguistics
	resources for use in non-technical undergraduate language science courses. By
	using the tool in conjunction with evidence-driven pedagogical case studies, we
	strive to provide opportunities for students to gain an understanding of
	linguistic concepts and analysis through the lens of realistic problems in
	feasible ways. Case studies tend to be used in legal, business, and health
	education contexts, but less in the teaching and learning of linguistics. The
	approach introduced also has potential to encourage students across training
	backgrounds to continue on to computational language analysis coursework.},
  url       = {http://www.aclweb.org/anthology/D17-2003}
}

@InProceedings{falke-gurevych:2017:EMNLP2017Demos,
  author    = {Falke, Tobias  and  Gurevych, Iryna},
  title     = {GraphDocExplore: A Framework for the Experimental Comparison of Graph-based Document Exploration Techniques},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {19--24},
  abstract  = {Graphs have long been proposed as a tool to browse and navigate in a collection
	of documents in order to support exploratory search. Many techniques to
	automatically extract different types of graphs, showing for example entities
	or concepts and different relationships between them, have been suggested.
	While experimental evidence that they are indeed helpful exists for some of
	them, it is largely unknown which type of graph is most helpful for a specific
	exploratory task. However, carrying out experimental comparisons with human
	subjects is challenging and time-consuming. Towards this end, we present the
	\textit{GraphDocExplore} framework. It provides an intuitive web interface for
	graph-based document exploration that is optimized for experimental user
	studies. Through a generic graph interface, different methods to extract graphs
	from text can be plugged into the system. Hence, they can be compared at
	minimal implementation effort in an environment that ensures controlled
	comparisons. The system is publicly available under an open-source license.},
  url       = {http://www.aclweb.org/anthology/D17-2004}
}

@InProceedings{stahlberg-EtAl:2017:EMNLP2017Demos,
  author    = {Stahlberg, Felix  and  Hasler, Eva  and  Saunders, Danielle  and  Byrne, Bill},
  title     = {SGNMT -- A Flexible NMT Decoding Platform for Quick Prototyping of New Models and Search Strategies},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {25--30},
  abstract  = {This paper introduces SGNMT, our experimental platform for machine translation
	research. SGNMT provides a generic interface to neural and symbolic scoring
	modules (predictors) with left-to-right semantic such as translation models
	like NMT, language models, translation lattices, n-best lists or other kinds of
	scores and constraints. Predictors can be combined with other predictors to
	form complex decoding tasks. SGNMT implements a number of search strategies for
	traversing the space spanned by the predictors which are appropriate for
	different predictor constellations. Adding new predictors or decoding
	strategies is particularly easy, making it a very efficient tool for
	prototyping new research ideas. SGNMT is actively being used by students in the
	MPhil program in Machine Learning, Speech and Language Technology at the
	University of Cambridge for course work and theses, as well as for most of the
	research work in our group.},
  url       = {http://www.aclweb.org/anthology/D17-2005}
}

@InProceedings{yanai-EtAl:2017:EMNLP2017Demos,
  author    = {Yanai, Kohsuke  and  Sato, Misa  and  Yanase, Toshihiko  and  Kurotsuchi, Kenzo  and  Koreeda, Yuta  and  Niwa, Yoshiki},
  title     = {StruAP: A Tool for Bundling Linguistic Trees through Structure-based Abstract Pattern},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {31--36},
  abstract  = {We present a tool for developing tree structure
	patterns that makes it easy to define
	the relations among textual phrases and
	create a search index for these newly defined
	relations. By using the proposed
	tool, users develop tree structure patterns
	through abstracting syntax trees. The tool
	features (1) intuitive pattern syntax, (2)
	unique functions such as recursive call of
	patterns and use of lexicon dictionaries,
	and (3) whole workflow support for relation
	development and validation. We report
	the current implementation of the tool
	and its effectiveness.},
  url       = {http://www.aclweb.org/anthology/D17-2006}
}

@InProceedings{mechanic-EtAl:2017:EMNLP2017Demos,
  author    = {Mechanic, Ross  and  Fulgoni, Dean  and  Cutler, Hannah  and  Rajana, Sneha  and  Liu, Zheyuan  and  Jackson, Bradley  and  Cocos, Anne  and  Callison-Burch, Chris  and  Apidianaki, Marianna},
  title     = {KnowYourNyms? A Game of Semantic Relationships},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {37--42},
  abstract  = {Semantic relation knowledge is crucial for natural language understanding. We
	introduce "KnowYourNyms?", a web-based game for learning semantic relations.
	While providing users with an engaging experience, the application collects
	large amounts of data that can be used to improve semantic relation
	classifiers. The data also broadly informs us of how people perceive the
	relationships between words, providing useful insights for research in
	psychology and linguistics.},
  url       = {http://www.aclweb.org/anthology/D17-2007}
}

@InProceedings{akbik-vollgraf:2017:EMNLP2017Demos,
  author    = {Akbik, Alan  and  Vollgraf, Roland},
  title     = {The Projector: An Interactive Annotation Projection Visualization Tool},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {43--48},
  abstract  = {Previous works proposed annotation projection in parallel corpora to
	inexpensively generate treebanks or propbanks for new languages. In this
	approach, linguistic annotation is automatically transferred from a
	resource-rich source language (SL) to translations in a target language (TL).
	However, annotation projection may be adversely affected by translational
	divergences between specific language pairs. For this reason, previous work
	often required careful qualitative analysis of projectability of specific
	annotation in order to define strategies to address quality and coverage
	issues. In this demonstration, we present THE PROJECTOR, an interactive GUI
	designed to assist researchers in such analysis: it allows users to execute and
	visually inspect annotation projection in a range of different settings. We
	give an overview of the GUI, discuss use cases and illustrate how the tool can
	facilitate discussions with the research community.},
  url       = {http://www.aclweb.org/anthology/D17-2008}
}

@InProceedings{sarnat-EtAl:2017:EMNLP2017Demos,
  author    = {Sarnat, Aaron  and  Joshi, Vidur  and  Petrescu-Prahova, Cristian  and  Herrasti, Alvaro  and  Stilson, Brandon  and  Hopkins, Mark},
  title     = {Interactive Visualization for Linguistic Structure},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {49--54},
  abstract  = {We provide a visualization library and web interface for interactively
	exploring a parse tree or a forest of parses. The library is not tied to any
	particular linguistic representation, but provides a general-purpose API for
	the interactive exploration of hierarchical linguistic structure. To facilitate
	rapid understanding of a complex structure, the API offers several important
	features, including expand/collapse functionality, positional and color cues,
	explicit visual support for sequential structure, and dynamic highlighting to
	convey node-to-text correspondence.},
  url       = {http://www.aclweb.org/anthology/D17-2009}
}

@InProceedings{schwartz-EtAl:2017:EMNLP2017Demos,
  author    = {Schwartz, H. Andrew  and  Giorgi, Salvatore  and  Sap, Maarten  and  Crutchley, Patrick  and  Ungar, Lyle  and  Eichstaedt, Johannes},
  title     = {DLATK: Differential Language Analysis ToolKit},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {55--60},
  abstract  = {We present Differential Language Analysis Toolkit (DLATK), an
	open-source python package and command-line tool developed for conducting
	social-scientific language analyses. 
	While DLATK provides standard NLP pipeline steps such as tokenization or
	SVM-classification, its novel strengths lie in analyses useful for
	psychological, health, and social science:  
	(1) incorporation of extra-linguistic structured information, 
	(2) specified levels and units of analysis (e.g. document, user, community),
	(3) statistical metrics for continuous outcomes, and 
	(4) robust, proven, and accurate pipelines for social-scientific prediction
	problems. 
	DLATK integrates multiple popular packages (SKLearn, Mallet), enables
	interactive usage (Jupyter Notebooks), and generally follows object oriented
	principles to make it easy to tie in additional libraries or storage
	technologies.},
  url       = {http://www.aclweb.org/anthology/D17-2010}
}

@InProceedings{abujabal-EtAl:2017:EMNLP2017Demos,
  author    = {Abujabal, Abdalghani  and  Saha Roy, Rishiraj  and  Yahya, Mohamed  and  Weikum, Gerhard},
  title     = {QUINT: Interpretable Question Answering over Knowledge Bases},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {61--66},
  abstract  = {We present QUINT, a live system for question answering over knowledge bases.
	QUINT automatically learns role-aligned utterance-query templates from user
	questions paired with their answers. When QUINT answers a question, it
	visualizes the complete derivation sequence from the natural language utterance
	to the final answer. The derivation provides an explanation of how the
	syntactic structure of the question was used to derive the structure of a
	SPARQL query, and how the phrases in the question were used to instantiate
	different parts of the query. When an answer seems unsatisfactory, the
	derivation provides valuable insights towards reformulating the question.},
  url       = {http://www.aclweb.org/anthology/D17-2011}
}

@InProceedings{richardson-kuhn:2017:EMNLP2017Demos,
  author    = {Richardson, Kyle  and  Kuhn, Jonas},
  title     = {Function Assistant: A Tool for NL Querying of APIs},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {67--72},
  abstract  = {In this paper, we describe Function Assistant, a lightweight Python-based
	toolkit for querying and exploring source code repositories using natural
	language. The toolkit is designed to help end-users of a target API quickly
	find information about functions through high-level natural language queries,
	or descriptions. For a given text query and background API, the tool finds
	candidate functions by performing a translation from the text to known
	representations in the API using the semantic parsing approach of (Richardson
	and Kuhn, 2017). Translations are automatically learned from example
	text-code pairs in example APIs. The toolkit includes features for building
	translation pipelines and query engines for arbitrary source code projects. To
	explore this last feature, we perform new experiments on 27 well-known Python
	projects hosted on Github.},
  url       = {http://www.aclweb.org/anthology/D17-2012}
}

@InProceedings{huang-EtAl:2017:EMNLP2017Demos,
  author    = {Huang, Chieh-Yang  and  Labetoulle, Tristan  and  Huang, Ting-Hao  and  Chen, Yi-Pei  and  Chen, Hung-Chen  and  Srivastava, Vallari  and  Ku, Lun-Wei},
  title     = {MoodSwipe: A Soft Keyboard that Suggests MessageBased on User-Specified Emotions},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {73--78},
  abstract  = {We present MoodSwipe, a soft keyboard that suggests text messages given the
	user-specified emotions utilizing the real dialog data. The aim of MoodSwipe is
	to create a convenient user interface to enjoy the technology of emotion
	classification and text suggestion, and at the same time to collect labeled
	data automatically for developing more advanced technologies.
	While users select the MoodSwipe keyboard, they can type as usual but sense the
	emotion conveyed by their text and receive suggestions for their message as a
	benefit. In MoodSwipe, the detected emotions serve as the medium for suggested
	texts, where viewing the latter is the incentive to correcting the former. We
	conduct several experiments to show the superiority of the emotion
	classification models trained on the dialog data, and further to verify good
	emotion cues are important context for text suggestion.},
  url       = {http://www.aclweb.org/anthology/D17-2013}
}

@InProceedings{miller-EtAl:2017:EMNLP2017Demos,
  author    = {Miller, Alexander  and  Feng, Will  and  Batra, Dhruv  and  Bordes, Antoine  and  Fisch, Adam  and  Lu, Jiasen  and  Parikh, Devi  and  Weston, Jason},
  title     = {ParlAI: A Dialog Research Software Platform},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {79--84},
  abstract  = {We introduce ParlAI (pronounced “par-lay”), an open-source software
	platform for dialog research implemented in Python, available at
	http://parl.ai. 
	Its goal is to provide a unified framework for sharing, training and testing
	dialog models; integration of Amazon Mechanical Turk for data collection, human
	evaluation, and online/reinforcement learning; and a repository of machine
	learning models for comparing with others' models, and improving upon existing
	architectures.
	Over 20 tasks are supported in the first
	release, including popular datasets such as SQuAD, bAbI tasks, MCTest, WikiQA,
	QACNN, QADailyMail, CBT, bAbI Dialog, Ubuntu, OpenSubtitles and VQA.
	Several models are integrated, including neural models such as memory networks,
	seq2seq and attentive LSTMs.},
  url       = {http://www.aclweb.org/anthology/D17-2014}
}

@InProceedings{richter-EtAl:2017:EMNLP2017Demos,
  author    = {Richter, Ludwig  and  Gei{\ss}, Johanna  and  Spitz, Andreas  and  Gertz, Michael},
  title     = {HeidelPlace: An Extensible Framework for Geoparsing},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {85--90},
  abstract  = {Geographic information extraction from textual data sources, called geoparsing,
	is a key task in text processing and central to subsequent spatial analysis
	approaches. Several geoparsers are available that support this task, each with
	its own (often limited or specialized) gazetteer and its own approaches to
	toponym detection and resolution. In this demonstration paper, we present
	HeidelPlace, an extensible framework in support of geoparsing. Key features of
	HeidelPlace include a generic gazetteer model that supports the integration of
	place information from different knowledge bases, and a pipeline approach that
	enables an effective combination of diverse modules tailored to specific
	geoparsing tasks. This makes HeidelPlace a valuable tool for testing and
	evaluating different gazetteer sources and geoparsing methods. In the
	demonstration, we show how to set up a geoparsing workflow with HeidelPlace and
	how it can be used to compare and consolidate the output of different
	geoparsing approaches.},
  url       = {http://www.aclweb.org/anthology/D17-2015}
}

@InProceedings{panchenko-EtAl:2017:EMNLP2017Demos,
  author    = {Panchenko, Alexander  and  Marten, Fide  and  Ruppert, Eugen  and  Faralli, Stefano  and  Ustalov, Dmitry  and  Ponzetto, Simone Paolo  and  Biemann, Chris},
  title     = {Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {91--96},
  abstract  = {Interpretability of a predictive model is a powerful feature that gains the
	trust of users in the correctness of the predictions.  In word sense
	disambiguation (WSD), knowledge-based systems tend to be much more
	interpretable than knowledge-free counterparts as they rely on the wealth of
	manually-encoded elements representing word senses, such as hypernyms, usage
	examples, and images. We present a WSD system that bridges the gap between
	these two so far disconnected groups of methods. Namely, our system, providing
	access to several state-of-the-art WSD models, aims to be interpretable as a
	knowledge-based system while it remains completely unsupervised and
	knowledge-free. The presented tool features a Web interface for all-word
	disambiguation of texts that makes the sense predictions human readable by
	providing interpretable word sense inventories, sense representations, and
	disambiguation results. We provide a public API, enabling seamless integration.},
  url       = {http://www.aclweb.org/anthology/D17-2016}
}

@InProceedings{dernoncourt-lee-szolovits:2017:EMNLP2017Demos,
  author    = {Dernoncourt, Franck  and  Lee, Ji Young  and  Szolovits, Peter},
  title     = {NeuroNER: an easy-to-use program for named-entity recognition based on neural networks},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {97--102},
  abstract  = {Named-entity recognition (NER) aims at identifying entities of interest in a
	text. Artificial neural networks (ANNs) have recently been shown to outperform
	existing NER systems. However, ANNs remain challenging to use for non-expert
	users. In this paper, we present NeuroNER, an easy-to-use named-entity
	recognition tool based on ANNs.  Users can annotate entities using a graphical
	web-based user interface (BRAT): the annotations are then used to train an ANN,
	which in turn predict entities' locations and categories in new texts. NeuroNER
	 makes this annotation-training-prediction flow smooth and accessible to
	anyone.},
  url       = {http://www.aclweb.org/anthology/D17-2017}
}

@InProceedings{papandrea-raganato-dellibovi:2017:EMNLP2017Demos,
  author    = {Papandrea, Simone  and  Raganato, Alessandro  and  Delli Bovi, Claudio},
  title     = {SupWSD: A Flexible Toolkit for Supervised Word Sense Disambiguation},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {103--108},
  abstract  = {In this demonstration we present SupWSD, a Java API for supervised Word Sense
	Disambiguation (WSD). This toolkit includes the implementation of a
	state-of-the-art supervised WSD system, together with a Natural Language
	Processing pipeline for preprocessing and feature extraction. Our aim is to
	provide an easy-to-use tool for the research community, designed to be modular,
	fast and scalable for training and testing on large datasets. The source code
	of SupWSD is available at http://github.com/SI3P/SupWSD.},
  url       = {http://www.aclweb.org/anthology/D17-2018}
}

@InProceedings{shapira-EtAl:2017:EMNLP2017Demos,
  author    = {Shapira, Ori  and  Ronen, Hadar  and  Adler, Meni  and  Amsterdamer, Yael  and  Bar-Ilan, Judit  and  Dagan, Ido},
  title     = {Interactive Abstractive Summarization for Event News Tweets},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {109--114},
  abstract  = {We present a novel interactive summarization system that is based on
	abstractive summarization, derived from a recent consolidated knowledge
	representation for multiple texts. We incorporate a couple of interaction
	mechanisms, providing a bullet-style summary while allowing to attain the most
	important information first and interactively drill down to more specific
	details. A usability study of our implementation, for event news tweets,
	suggests the utility of our approach for text exploration.},
  url       = {http://www.aclweb.org/anthology/D17-2019}
}

@InProceedings{abzianidze:2017:EMNLP2017Demos,
  author    = {Abzianidze, Lasha},
  title     = {LangPro: Natural Language Theorem Prover},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {115--120},
  abstract  = {LangPro is an automated theorem prover for natural language.
	Given a set of premises and a hypothesis, it is able to prove semantic
	relations between them.
	The prover is based on a version of analytic tableau method specially designed
	for natural logic.
	The proof procedure operates on logical forms that preserve linguistic
	expressions to a large extent. 
	%This property makes the logical forms easily obtainable from syntactic trees.
	%, in particular, Combinatory Categorial Grammar derivation trees.   
	The nature of proofs is deductive and transparent.
	On the FraCaS and SICK textual entailment datasets, the prover achieves high
	results comparable to state-of-the-art.},
  url       = {http://www.aclweb.org/anthology/D17-2020}
}

@InProceedings{lee-shin-kim:2017:EMNLP2017Demos,
  author    = {Lee, Jaesong  and  Shin, Joong-Hwi  and  Kim, Jun-Seok},
  title     = {Interactive Visualization and Manipulation of Attention-based Neural Machine Translation},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {121--126},
  abstract  = {While neural machine translation (NMT) provides high-quality translation, it is
	still hard to interpret and analyze its behavior. We present an interactive
	interface for visualizing and intervening behavior of NMT, specifically
	concentrating on the behavior of beam search mechanism and attention component.
	The tool (1) visualizes search tree and attention and (2) provides interface to
	adjust search tree and attention weight (manually or automatically) at
	real-time. We show the tool gives various methods to understand NMT.},
  url       = {http://www.aclweb.org/anthology/D17-2021}
}