Proceedings of the 2019 Conference of the North

24 papers
Abbreviation Explorer - an interactive system for pre-evaluation of Unsupervised Abbreviation Disambiguation (N19-4)

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Challenge: Abbreviation Explorer helps to identify long-forms that are easily confused . it can also pinpoint likely causes such as limitations of normalization, language switching, or inconsistent typing.
Approach: They propose a system that supports interactive exploration of abbreviations that are challenging for Unsupervised Abbreviation Disambiguation.
Outcome: The proposed system can identify long-forms that are easily confused and pinpoint likely causes . it can also identify which long-terms would benefit from additional input text . the proposed rules can be easily applied to existing vector spaces to improve performance while avoiding the cost of retraining.
ADIDA: Automatic Dialect Identification for Arabic (N19-4)

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Challenge: Demo paper describes a web-based system for automatic dialect identification for Arabic text.
Approach: They present a web-based system for automatic dialect identification for Arabic text that distinguishes between 25 Arab cities and Modern Standard Arabic.
Outcome: The proposed system distinguishes among the dialects of 25 Arab cities (from Rabat to Muscat) and Modern Standard Arabic (MSA).
Enabling Search and Collaborative Assembly of Causal Interactions Extracted from Multilingual and Multi-domain Free Text (N19-4)

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Challenge: a new searchable knowledge graph allows users to search for causal interactions in multiple languages . a recent study shows that search tools are shallow and do not support multilingual research .
Approach: They propose a system that integrates causal interactions into a single searchable knowledge graph.
Outcome: The proposed system extracts over 600 thousand causal statements from 120 thousand Portuguese publications with a precision of 62%.
INS: An Interactive Chinese News Synthesis System (N19-4)

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Challenge: In the last decade, news websites and apps become more popular, which can provide us an extremely large volume of news articles.
Approach: They propose a system which automatically synthesizes news articles into a long overview article by interacting with users.
Outcome: The proposed system can generate news overview articles automatically or by interacting with users.
Learning to Respond to Mixed-code Queries using Bilingual Word Embeddings (N19-4)

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Challenge: Many queries are submitted to search engines on the Web every day to retrieve linguistic information for learning a second language (L2) due to limited L2 vocabulary knowledge, users often submit mix-coded queries without converting them into target language queries.
Approach: They propose a method for learning bilingual word embeddings to support second language learners . mixed-code queries are transformed into target language queries . preliminary evaluation shows method performs reasonablly well .
Outcome: The proposed method performs reasonablly well on a list of common word-translation queries.
Train, Sort, Explain: Learning to Diagnose Translation Models (N19-4)

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Challenge: Evaluating translation models is a trade-off between effort and detail.
Approach: They propose to use a neural text classifier to automatically expose systematic differences between human and machine translations to human experts.
Outcome: The proposed method exposes systematic differences between human and machine translations to human experts.
compare-mt: A Tool for Holistic Comparison of Language Generation Systems (N19-4)

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Challenge: Unlike machine translation, natural language outputs are nuanced and there are no clear yes/no distinctions about whether they are correct or not.
Approach: They describe compare-mt, a tool for holistic analysis and comparison of the results of systems for language generation tasks such as machine translation.
Outcome: The compare-mt tool is an open-source pure-python package that has already proven useful to generate analyses that have been used in our papers.
Eidos, INDRA, & Delphi: From Free Text to Executable Causal Models (N19-4)

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Challenge: a paper proposes a method for building probabilistic models of complex phenomena such as food insecurity . currently, these models are hand-built for each new situation and require months to construct .
Approach: They propose an approach that builds executable probabilistic models from raw, free text.
Outcome: The proposed approach builds executable probabilistic models from raw, free text.
fairseq: A Fast, Extensible Toolkit for Sequence Modeling (N19-4)

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Challenge: OpenNMT is a community-built toolkit written in multiple languages with an emphasis on extensibility.
Approach: They propose to use PyTorch to train custom sequence models for translation, summarization, language modeling, and other tasks.
Outcome: The proposed toolkit is fast, extensible, and useful for both research and production.
FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP (N19-4)

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Challenge: Existing approaches combine word embeddings with character-level features to model additional features such as subword structures and meaning ambiguity.
Approach: They present FLAIR, an NLP framework that enables embeddings of word and document data . they propose a hierarchical learning architecture that concatenates output states of a character-level CNN or RNN with the output states from a task data.
Outcome: The proposed framework hides embedding-specific engineering complexity and allows researchers to "mix and match" various embeddables with little effort.
ChatEval: A Tool for Chatbot Evaluation (N19-4)

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Challenge: open-domain dialog systems are difficult to evaluate due to lack of standardization and standardization in evaluation procedures.
Approach: They propose a framework for human evaluation of chatbots that augments existing tools . researchers can submit their trained models to the ChatEval web interface . reproducibility and model assessment for opendomain dialog systems is challenging .
Outcome: The proposed framework provides a web-based hub for researchers to compare their models with baselines and prior work.
LeafNATS: An Open-Source Toolkit and Live Demo System for Neural Abstractive Text Summarization (N19-4)

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Challenge: Neural abstractive text summarization (NATS) has gained a lot of attention in the past few years from both industry and academia.
Approach: They propose an open-source toolkit 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.
Outcome: The proposed model can be used to generate high-quality summaries that are verbally innovative and can easily incorporate external knowledge.
End-to-End Open-Domain Question Answering with BERTserini (N19-4)

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Challenge: a new open-domain question answering system integrates best practices from IR with a BERT-based reader to identify answers from a large corpus of Wikipedia articles.
Approach: They propose an end-to-end question answering system that integrates BERT with an IR reader.
Outcome: The proposed system improves on a standard benchmark test collection.
FAKTA: An Automatic End-to-End Fact Checking System (N19-4)

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Challenge: Existing studies have investigated individual components of fact checking process but none offer such a capability.
Approach: They propose a framework that integrates various components of a fact-checking process.
Outcome: The proposed framework integrates various components of a fact-checking process to predict the factuality of claims and provide evidence at the document and sentence level to explain its predictions.
iComposer: An Automatic Songwriting System for Chinese Popular Music (N19-4)

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Challenge: iComposer is an interactive web-based songwriting system designed to assist human creators by greatly simplifying music production.
Approach: They propose a web-based songwriting system that automatically generates melody from text . they use sequence-to-sequence models to predict melody, rhythm, and lyrics .
Outcome: The proposed system can write pleasing melodies and meaningful lyrics similar to humans.
Plan, Write, and Revise: an Interactive System for Open-Domain Story Generation (N19-4)

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Challenge: a neural narrative generation system interacts with humans to generate stories . a recent resurgence of interest in collaborative storytelling has led to new approaches .
Approach: They propose a neural narrative generation system that interacts with humans to generate stories.
Outcome: The proposed system improves story quality and user engagement under time constraints.
LT Expertfinder: An Evaluation Framework for Expert Finding Methods (N19-4)

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Challenge: LT Expertfinder is a web-based tool for expert finding and expert profiling.
Approach: They propose a web-application that enables qualitative comparison between different ranking methods . LT Expertfinder provides detailed expert profiles linked to Wikidata and Google Scholar .
Outcome: The LT Expertfinder is a web-based tool for expert finding and evaluation.
SkillBot: Towards Automatic Skill Development via User Demonstration (N19-4)

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Challenge: Existing industrial PA products require software developers to build new skills via IDE tools.
Approach: They propose a software that automatically develops a natural language understanding engine and implements the action without the need of coding.
Outcome: The proposed system performs well on both benchmark and in-house datasets.
Multilingual Entity, Relation, Event and Human Value Extraction (N19-4)

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Challenge: Existing systems that extract knowledge elements from multiple languages and documents do not aggregate knowledge from multiple documents and languages.
Approach: They propose a multilingual knowledge extraction system that performs entity discovery and linking, relation extraction, event extraction, and coreference.
Outcome: The proposed system performs entity discovery and linking, relation extraction, event extraction, and coreference.
Litigation Analytics: Extracting and querying motions and orders from US federal courts (N19-4)

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Challenge: a detailed manual analysis of a docket could provide valuable information for the suit and the respective judge.
Approach: They applied machine learning and machine learning to extract and aggregate docket statistics . they used a search engine to query the data in real time and a question-answering interface .
Outcome: The proposed method extracts information from 8 million federal dockets and keeps up with newly closed docketes.
Community lexical access for an endangered polysynthetic language: An electronic dictionary for St. Lawrence Island Yupik (N19-4)

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Challenge: a new electronic dictionary for St. Lawrence Island Yupik is developed to facilitate language-learning on the island . the endangered language is spoken primarily on St. lisa's St.liss island, Alaska .
Approach: They propose a morphologically-aware electronic dictionary for St. Lawrence Island Yupik . the dictionary is set in an uncluttered interface and uses HTML, Javascript, and CSS .
Outcome: The proposed dictionary is set in an uncluttered interface and is available in English and in Yupik . it is based on the morphologically-aware version of the Badten et al. paper dictionary .
Visualizing Inferred Morphotactic Systems (N19-4)

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Challenge: a web-based system facilitates the exploration of complex morphological patterns found in morphology rich languages.
Approach: They propose a web-based system that facilitates the exploration of complex morphological patterns found in morphology rich languages.
Outcome: The proposed system can be used to explore morphological patterns in morphology rich languages.
A Research Platform for Multi-Robot Dialogue with Humans (N19-4)

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Challenge: a new research platform supports spoken dialogue interaction with multiple robots . a ground robot and an aerial robot are used to perform search and rescue tasks .
Approach: They propose a platform that supports spoken dialogue interaction with multiple robots . they use existing tools for speech recognition and dialogue management .
Outcome: The proposed platform supports spoken dialogue interaction with multiple robots in a search and rescue scenario.
Chat-crowd: A Dialog-based Platform for Visual Layout Composition (N19-4)

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Challenge: We present Chat-crowd, an interactive environment for visual layout composition via conversational interactions . system can be integrated with crowdsourcing platforms for both synchronous and asynchronous data collection .
Approach: They introduce an interactive environment for visual layout composition via conversational interactions that supports multiple agents with two conversational roles.
Outcome: The proposed system can be integrated with crowdsourcing platforms for both synchronous and asynchronous data collection and has quality controls on the performance of both types of agents.

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