List of accepted system demonstrations

The demonstrations will be presented as posters on Tuesday, June 2nd, from 17:00 to 20:00.

This schedule is interactive, you can click on a demonstration to view its abstract.

24 System Demonstrations

Mihai Surdeanu, Tom Hicks and Marco Antonio Valenzuela-Escarcega. "Two Practical Rhetorical Structure Theory Parsers"

We describe the design, development, and API for two discourse parsers for Rhetorical Structure Theory. The two parsers use the same underlying framework, but one uses features that rely on dependency syntax, produced by a fast shift-reduce parser, whereas the other uses a richer feature space, including both constituent- and dependency-syntax and coreference information, produced by the Stanford CoreNLP toolkit. Both parsers obtain state-of-the-art performance, and use a very simple API consisting of, minimally, two lines of Scala code. We accompany this code with a visualization library that runs the two parsers in parallel, and displays the two generated discourse trees side by side, which provides an intuitive way of comparing the two parsers.

Sebastian Martschat, Thierry Göckel and Michael Strube. "Analyzing and Visualizing Coreference Resolution Errors"

We present a toolkit for coreference resolution error analysis. It implements a recently proposed analysis framework and contains rich components for analyzing and visualizing recall and precision errors.

Anoop Kunchukuttan, Ratish Puduppully and Pushpak Bhattacharyya. "Brahmi-Net: A transliteration and script conversion system for languages of the Indian subcontinent"

We present Brahmi-Net - an online system for transliteration and script conversion for all major Indian language pairs (306 pairs). The system covers 13 Indo-Aryan languages, 4 Dravidian languages and English. For training the transliteration systems, we mined parallel transliteration corpora from parallel translation corpora using an unsupervised method and trained statistical transliteration systems using the mined corpora. Languages which do not have a parallel corpora are supported by transliteration through a bridge language. Our script conversion system supports conversion between all Brahmi-derived scripts as well as ITRANS romanization scheme. For this, we leverage co-ordinated Unicode ranges between Indic scripts and use an extended ITRANS encoding for transliterating between English and Indic scripts. The system also provides top-k transliterations and simultaneous transliteration into multiple output languages. We provide a Python as well as REST API to access these services. The API and the mined transliteration corpus are made available for research use under an open source license.

Markus Dreyer and Jonathan Graehl. "hyp: A Toolkit for Representing, Manipulating, and Optimizing Hypergraphs"

We present hyp, an open-source toolkit for the representation, manipulation, and optimization of weighted directed hypergraphs. hyp provides compose, project, invert functionality, k-best path algorithms, the inside and outside algorithms, and more. Finite-state machines are modeled as a special case of directed hypergraphs. hyp consists of a C++ API, as well as a command line tool, and is available for download at

Nanyun Peng, Francis Ferraro, Mo Yu, Nicholas Andrews, Jay DeYoung, Max Thomas, Matthew R. Gormley, Travis Wolfe, Craig Harman, Benjamin Van Durme and Mark Dredze. "A Concrete Chinese NLP Pipeline"

Natural language processing research increasingly relies on the output of a variety of syntactic and semantic analytics. Yet integrating output from multiple analytics into a single framework can be time consuming and slow research progress. We present a Chinese Concrete NLP Pipeline: an NLP stack built using a series of open source systems integrated based on the Concrete data schema. Our pipeline includes data ingest, word segmentation, part of speech tagging, parsing, named entity recognition, relation extraction and cross document coreference resolution. Additionally, we integrate a tool for visualizing these annotations as well as allowing for the manual annotation of new data. We release our pipeline to the research community to facilitate work on Chinese language tasks that require rich linguistic annotations.

Hubert Soyer, Goran Topić, Pontus Stenetorp and Akiko Aizawa. "CroVeWA: Crosslingual Vector-Based Writing Assistance"

We present an interactive web-based writing assistance system that is based on recent advances in crosslingual compositional distributed semantics. Given queries in Japanese or English, our system can retrieve semantically related sentences from high quality English corpora. By employing crosslingually constrained vector space models to represent phrases, our system naturally sidesteps several difficulties that would arise from direct word-to-text matching, and is able to provide novel functionality like the visualization of semantic relationships between phrases interlingually and intralingually.

Diane Napolitano, Kathleen Sheehan and Robert Mundkowsky. "Online Readability and Text Complexity Analysis with TextEvaluator"

We have developed the TextEvaluator system for providing text complexity and Common Core-aligned readability information. Detailed text complexity information is provided by eight component scores, presented in such a way as to aid in the user’s understanding of the overall readability metric, which is provided as a holistic score on a scale of 100 to 2000. The user may select a targeted US grade level and receive additional analysis relative to it. This and other capabilities are accessible via a feature-rich front-end, located at

Wencan Luo, Xiangmin Fan, Muhsin Menekse, Jingtao Wang and Diane Litman. "Enhancing Instructor-Student and Student-Student Interactions with Mobile Interfaces and Summarization"

Educational research has demonstrated that asking students to respond to reflection prompts can increase interaction between instructors and students, which in turn can improve both teaching and learning especially in large classrooms. However, administering an instructor's prompts, collecting the students' responses, and summarizing these responses for both instructors and students is challenging and expensive. To address these challenges, we have developed an application called CourseMIRROR (Mobile In-situ Reflections and Review with Optimized Rubrics). CourseMIRROR uses a mobile interface to administer prompts and collect reflective responses for a set of instructor-assigned course lectures. After collection, CourseMIRROR automatically summarizes the reflections with an extractive phrase summarization method, using a clustering algorithm to rank extracted phrases by student coverage. Finally, CourseMIRROR presents the phrase summary to both instructors and students to help them understand the difficulties and misunderstandings encountered.

Vincent Kríž and Barbora Hladka. "RExtractor: a Robust Information Extractor"

The RExtractor system is an information extractor that processes input documents by natural language processing tools and consequently queries the parsed sentences to extract a knowledge base of entities and their relations. The extraction queries are designed manually using a tool that enables natural graphical representation of queries over dependency trees. A workflow of the system is designed to be language and domain independent. We demonstrate RExtractor on Czech and English legal documents.

Lucy Vanderwende, Arul Menezes and Chris Quirk. "An AMR parser for English, French, German, Spanish and Japanese and a new AMR-annotated corpus"

In this demonstration, we will present our online parser that allows users to submit any sentence and obtain an analysis following the specification of AMR (Banarescu et al., 2014) to a large extent. This AMR analysis is generated by a small set of rules that convert a native Logical Form analysis provided by a pre-existing parser into the AMR format. While we demonstrate the performance of our AMR parser on data sets annotated by the LDC, we will focus attention in the demo on the following two areas: 1) we will make available AMR annotations for the data sets that were used to develop our parser, to serve as a supplement to the LDC data sets, and 2) we will demonstrate AMR parsers for German, French, Spanish and Japanese that make use of the same small set of LF-to-AMR conversion rules.

Yifan He and Ralph Grishman. "ICE: Rapid Information Extraction Customization for NLP Novices"

We showcase ICE, an Integrated Customization Environment for Information Extraction. ICE is an easy tool for non-NLP experts to rapidly build customized IE systems for a new domain.

Naomi Saphra and Adam Lopez. "AMRICA: an AMR Inspector for Cross-language Alignments"

Abstract Meaning Representation (AMR), an annotation scheme for natural language semantics, has drawn attention for its simplicity and representational power. Because AMR annotations are not designed for human readability, we present AMRICA, a visual aid for exploration of AMR annotations. AMRICA can visualize an AMR or the difference between two AMRs to help users diagnose interannotator disagreement or errors from an AMR parser. AMRICA can also automatically align and visualize the AMRs of a sentence and its translation in a parallel text. We believe AMRICA will simplify and streamline exploratory research on cross-lingual AMR corpora.

Dezhao Song, Frank Schilder, Charese Smiley and Chris Brew. "Natural Language Question Answering and Analytics for Diverse and Interlinked Datasets"

Previous systems for natural language questions over complex linked datasets require the user to enter a complete and well-formed question, and present the answers as raw lists of entities. Using a feature-based grammar with a full formal semantics, we have developed a system that is able to support rich autosuggest, and to deliver dynamically generated analytics for each result that it returns.

Yusuke Oda, Graham Neubig, Sakriani Sakti, Tomoki Toda and Satoshi Nakamura. "Ckylark: A More Robust PCFG-LA Parser"

This paper describes Ckylark, a PCFG-LA style phrase structure parser that is more robust than other parsers in the genre. PCFG-LA parsers are known to achieve highly competitive performance, but sometimes the parsing process fails completely, and no parses can be generated. Ckylark introduces three new techniques that prevent possible causes for parsing failure: outputting intermediate results when coarse-to-fine analysis fails, smoothing lexicon probabilities, and scaling probabilities to avoid underflow. An experiment shows that this allows millions of sentences can be parsed without any failures, in contrast to other publicly available PCFG-LA parsers. Ckylark is implemented in C++, and is available open-source under the LGPL license.

Xuchen Yao. "Lean Question Answering over Freebase from Scratch"

For the task of question answering (QA) over Freebase on the WebQuestions dataset (Berant et al., 2013), we found that 85% of all questions (in the training set) can be directly answered via a single binary relation. Thus we turned this task into slot-filling for <question topic, relation, answer> tuples: predicting relations to get answers given a question’s topic. We design efficient data structures to identify question topics organically from 46 million Freebase topic names, without employing any NLP processing tools. Then we present a lean QA system that runs in real time (in offline batch testing it answered two thousand questions in 51 seconds on a laptop). The system also achieved 7.8% better F1 score (harmonic mean of average precision and recall) than the previous state of the art.

Pablo Ruiz, Thierry Poibeau and Fréderique Mélanie. "ELCO3: Entity Linking with Corpus Coherence Combining Open Source Annotators"

Entity Linking (EL) systems' performance is uneven across corpora or depending on entity types. To help overcome this issue, we propose an EL workflow that combines the outputs of several open source EL systems, and selects annotations via weighted voting. The results are displayed on a UI that allows the users to navigate the corpus and to evaluate annotation quality based on several metrics.

Juhani Luotolahti, Jenna Kanerva, Sampo Pyysalo and Filip Ginter. "SETS: Scalable and Efficient Tree Search in Dependency Graphs"

We present a syntactic analysis query toolkit geared specifically towards massive dependency parsebanks and morphologically rich languages. The query language allows arbitrary tree queries, including negated branches, and is suitable for querying analyses with rich morphological annotation. Treebanks of over a million words can be comfortably queried on a low-end netbook, and a parsebank with over 100M words on a single consumer-grade server. We also introduce a web-based interface for interactive querying. All contributions are available under open licenses.

Sravana Reddy and James Stanford. "A Web Application for Automated Dialect Analysis"

Sociolinguists are regularly faced with the task of measuring phonetic features from speech, which involves manually transcribing audio recordings -- a major bottleneck to analyzing large collections of data. We harness automatic speech recognition to build an online end-to-end web application where users upload untranscribed speech collections and receive formant measurements of the vowels in their data. We demonstrate this tool by using it to automatically analyze President Barack Obama's vowel pronunciations.

Jim Chang and Jason Chang. "WriteAhead2: Mining Lexical Grammar Patterns for Assisted Writing"

This paper describes WriteAhead2, an interactive writing environment that provides lexical and grammatical suggestions for second language learners, and helps them write fluently and avoid common writing errors. The method involves learning phrase templates from dictionary examples, and extracting grammar patterns with example phrases from an academic corpus. At run-time, as the user types word after word, the actions trigger a list after list of suggestions. Each successive list contains grammar patterns and examples, most relevant to the half-baked sentence. WriteAhead2 facilitates steady, timely, and spot-on interactions between learner writers and relevant information for effective assisted writing. Preliminary experiments show that WriteAhead2 has the potential to induce better writing and improve writing skills.

Seonyeong Park, Soonchoul Kwon, Byungsoo Kim, Sangdo Han, Hyosup Shim, Gary Geunbae Lee and Gary Geunbae Lee. "Question Answering System using Multiple Information Source and Open Type Answer Merge"

This paper presents a multi-strategy and multi-source question answering (QA) system that can use multiple strategies to both answer natural language (NL) questions and respond to keywords. We use multiple information sources including curated knowledge base, raw text, auto-generated triples, and NL processing results. We develop open semantic answer type detector for answer merging and improve previous developed single QA modules such as knowledge base based QA, information retrieval based QA.

Mahmoud Azab, Chris Hokamp and Rada Mihalcea. "Using Word Semantics To Assist English as a Second Language Learners"

We introduce an interactive interface that aims to help English as a Second Language (ESL) students overcome language related hindrances while reading a text. The interface allows the user to find supplementary information on selected difficult words. The interface is empowered by our lexical substitution engine that provides context-based synonyms for difficult words. We also provide a practical solution for a real-world usage scenario. We demonstrate using the lexical substitution engine -- as a browser extension that can annotate and disambiguate difficult words on any webpage.

Juan Soler-Company, Miguel Ballesteros, Bernd Bohnet, Simon Mille and Leo Wanner. "Visualizing Deep-Syntactic Parser Output"

``Deep-syntactic" dependency structures bridge the gap between the surface-syntactic structures as produced by state-of-the-art dependency parsers and semantic logical forms in that they abstract away from surface-syntactic idiosyncrasies, but still keep the linguistic structure of a sentence. They have thus a great potential for such downstream applications as machine translation and summarization. In this demo paper, we propose an online version of a deep-syntactic parser that outputs deep-syntactic structures from plain sentences and visualizes them using the Brat tool. Along with the deep-syntactic structures, the user can also inspect the visual presentation of the surface-syntactic structures that serve as input to the deep-syntactic parser and that are produced by the joint tagger and syntactic transition-based parser ran in the pipeline before the deep-syntactic parser.

Sameer Singh, Tim Rocktäschel, Luke Hewitt, Jason Naradowsky and Sebastian Riedel. "WOLFE: An NLP-friendly Declarative Machine Learning Stack"

Developing machine learning algorithms for natural language processing (NLP) applications is inherently an iterative process, involving a continuous refinement of the choice of model, engineering of features, selection of inference algorithms, search for the right hyper- parameters, and error analysis. Existing probabilistic program languages (PPLs) only provide partial solutions; most of them do not support commonly used models such as matrix factorization or neural networks, and do not facilitate interactive and iterative programming that is crucial for rapid development of these models.

In this demo we introduce WOLFE, a stack designed to facilitate the development of NLP applications: (1) the WOLFE language allows the user to concisely define complex models, enabling easy modification and extension, (2) the WOLFE interpreter transforms declarative ma- chine learning code into automatically differentiable terms or, where applicable, into factor graphs that allow for complex models to be applied to real-world applications, and (3) the WOLFE IDE provides a number of different visual and interactive elements, allowing intuitive exploration and editing of the data representations, the underlying graphical models, and the execution of the inference algorithms.

Mohammad Taher Pilehvar and Roberto Navigli. "An Open-source Framework for Multi-level Semantic Similarity Measurement"

We present an open source, freely available Java implementation of Align, Disambiguate, and Walk (ADW), a state-of-the-art approach for measuring semantic similarity based on the Personalized PageRank algorithm. A pair of linguistic items, such as phrases or sentences, are first disambiguated using an alignment-based disambiguation technique and then modeled using random walks on the WordNet graph. ADW provides three main advantages: (1) it is applicable to all types of linguistic items, from word senses to texts; (2) it is all-in-one, i.e., it does not need any additional resource, training or tuning; and (3) it has proven to be highly reliable at different lexical levels and multiple evaluation benchmarks. We are releasing the source code at We also provide at a Web interface and a Java API that can be seamlessly integrated into other NLP systems requiring semantic similarity measurement.