ymoslem/acl-abstracts huggingface dataset:
Demonstrating Ambient Search: Implicit Document Retrieval for Speech Streams (2016)

In this demonstration paper we describe Ambient Search, a system that displays and retrieves documents in real time based on speech input. The system operates continuously in ambient mode, i.e. it generates speech transcriptions and identifies main keywords and keyphrases, while also querying its index to display relevant documents without explicit query. Without user intervention, the results are dynamically updated; users can choose to interact with the system at any time, employing a conversation protocol that is enriched with the ambient information gathered continuously. Our evaluation shows that Ambient Search outperforms another implicit speech-based information retrieval system. Ambient search is available as open source software.
ConFarm: Extracting Surface Representations of Verb and Noun Constructions from Dependency Annotated Corpora of Russian (2016)

ConFarm is a web service dedicated to extraction of surface representations of verb and noun constructions from dependency annotated corpora of Russian texts. Currently, the extraction of constructions with a specific lemma from SynTagRus and Russian National Corpus is available. The system provides flexible interface that allows users to fine-tune the output. Extracted constructions are grouped by their contents to allow for compact representation, and the groups are visualised as a graph in order to help navigating the extraction results. ConFarm differs from similar existing tools for Russian language in that it offers full constructions, as opposed to extracting separate dependents of search word or working with collocations, and allows users to discover unexpected constructions as opposed to searching for examples of a user-defined construction.
ENIAM: Categorial Syntactic-Semantic Parser for Polish (2016)

This paper presents ENIAM, the first syntactic and semantic parser that generates semantic representations for sentences in Polish. The parser processes non-annotated data and performs tokenization, lemmatization, dependency recognition, word sense annotation, thematic role annotation, partial disambiguation and computes the semantic representation.
Towards Non-projective High-Order Dependency Parser (2016)

This paper presents a novel high-order dependency parsing framework that targets non-projective treebanks. It imitates how a human parses sentences in an intuitive way. At every step of the parse, it determines which word is the easiest to process among all the remaining words, identifies its head word and then folds it under the head word. Further, this work is flexible enough to be augmented with other parsing techniques.
Using Synthetically Collected Scripts for Story Generation (2016)

A script is a type of knowledge representation in artificial intelligence (AI). This paper presents two methods for synthetically using collected scripts for story generation. The first method recursively generates long sequences of events and the second creates script networks. Although related studies generally use one or more scripts for story generation, this research synthetically uses many scripts to flexibly generate a diverse narrative.
CaseSummarizer: A System for Automated Summarization of Legal Texts (2016)

Attorneys, judges, and others in the justice system are constantly surrounded by large amounts of legal text, which can be difficult to manage across many cases. We present CaseSummarizer, a tool for automated text summarization of legal documents which uses standard summary methods based on word frequency augmented with additional domain-specific knowledge. Summaries are then provided through an informative interface with abbreviations, significance heat maps, and other flexible controls. It is evaluated using ROUGE and human scoring against several other summarization systems, including summary text and feedback provided by domain experts.
WISDOM X, DISAANA and D-SUMM: Large-scale NLP Systems for Analyzing Textual Big Data (2016)

We demonstrate our large-scale NLP systems: WISDOM X, DISAANA, and D-SUMM. WISDOM X provides numerous possible answers including unpredictable ones to widely diverse natural language questions to provide deep insights about a broad range of issues. DISAANA and D-SUMM enable us to assess the damage caused by large-scale disasters in real time using Twitter as an information source.
Multilingual Information Extraction with PolyglotIE (2016)

We present PolyglotIE, a web-based tool for developing extractors that perform Information Extraction (IE) over multilingual data. Our tool has two core features: First, it allows users to develop extractors against a unified abstraction that is shared across a large set of natural languages. This means that an extractor needs only be created once for one language, but will then run on multilingual data without any additional effort or language-specific knowledge on part of the user. Second, it embeds this abstraction as a set of views within a declarative IE system, allowing users to quickly create extractors using a mature IE query language. We present PolyglotIE as a hands-on demo in which users can experiment with creating extractors, execute them on multilingual text and inspect extraction results. Using the UI, we discuss the challenges and potential of using unified, crosslingual semantic abstractions as basis for downstream applications. We demonstrate multilingual IE for 9 languages from 4 different language groups: English, German, French, Spanish, Japanese, Chinese, Arabic, Russian and Hindi.
WordForce: Visualizing Controversial Words in Debates (2016)

This paper presents WordForce, a system powered by the state of the art neural network model to visualize the learned user-dependent word embeddings from each post according to the post content and its engaged users. It generates the scatter plots to show the force of a word, i.e., whether the semantics of word embeddings from posts of different stances are clearly separated from the aspect of this controversial word. In addition, WordForce provides the dispersion and the distance of word embeddings from posts of different stance groups, and proposes the most controversial words accordingly to show clues to what people argue about in a debate.
Zara: A Virtual Interactive Dialogue System Incorporating Emotion, Sentiment and Personality Recognition (2016)

Zara, or {\textquoteleft}Zara the Supergirl' is a virtual robot, that can exhibit empathy while interacting with an user, with the aid of its built in facial and emotion recognition, sentiment analysis, and speech module. At the end of the 5-10 minute conversation, Zara can give a personality analysis of the user based on all the user utterances. We have also implemented a real-time emotion recognition, using a CNN model that detects emotion from raw audio without feature extraction, and have achieved an average of 65.7{\%} accuracy on six different emotion classes, which is an impressive 4.5{\%} improvement from the conventional feature based SVM classification. Also, we have described a CNN based sentiment analysis module trained using out-of-domain data, that recognizes sentiment from the speech recognition transcript, which has a 74.8 F-measure when tested on human-machine dialogues.
NL2KB: Resolving Vocabulary Gap between Natural Language and Knowledge Base in Knowledge Base Construction and Retrieval (2016)

Words to express relations in natural language (NL) statements may be different from those to represent properties in knowledge bases (KB). The vocabulary gap becomes barriers for knowledge base construction and retrieval. With the demo system called NL2KB in this paper, users can browse which properties in KB side may be mapped to for a given relational pattern in NL side. Besides, they can retrieve the sets of relational patterns in NL side for a given property in KB side. We describe how the mapping is established in detail. Although the mined patterns are used for Chinese knowledge base applications, the methodology can be extended to other languages.
PKUSUMSUM : A Java Platform for Multilingual Document Summarization (2016)

PKUSUMSUM is a Java platform for multilingual document summarization, and it sup-ports multiple languages, integrates 10 automatic summarization methods, and tackles three typical summarization tasks. The summarization platform has been released and users can easily use and update it. In this paper, we make a brief description of the char-acteristics, the summarization methods, and the evaluation results of the platform, and al-so compare PKUSUMSUM with other summarization toolkits.
Kotonush: Understanding Concepts Based on Values behind Social Media (2016)

Kotonush, a system that clarifies people`s values on various concepts on the basis of what they write about on social media, is presented. The values are represented by ordering sets of concepts (e.g., London, Berlin, and Rome) in accordance with a common attribute intensity expressed by an adjective (e.g., entertaining). We exploit social media text written by different demographics and at different times in order to induce specific orderings for comparison. The system combines a text-to-ordering module with an interactive querying interface enabled by massive hyponymy relations and provides mechanisms to compare the induced orderings from various viewpoints. We empirically evaluate Kotonush and present some case studies, featuring real-world concept orderings with different domains on Twitter, to demonstrate the usefulness of our system.
Exploring a Continuous and Flexible Representation of the Lexicon (2016)

We aim at showing that lexical descriptions based on multifactorial and continuous models can be used by linguists and lexicographers (and not only by machines) so long as they are provided with a way to efficiently navigate data collections. We propose to demonstrate such a system.
Automatically Suggesting Example Sentences of Near-Synonyms for Language Learners (2016)

In this paper, we propose GiveMeExample that ranks example sentences according to their capacity of demonstrating the differences among English and Chinese near-synonyms for language learners. The difficulty of the example sentences is automatically detected. Furthermore, the usage models of the near-synonyms are built by the GMM and Bi-LSTM models to suggest the best elaborative sentences. Experiments show the good performance both in the fill-in-the-blank test and on the manually labeled gold data, that is, the built models can select the appropriate words for the given context and vice versa.
Kyoto-NMT: a Neural Machine Translation implementation in Chainer (2016)

We present Kyoto-NMT, an open-source implementation of the Neural Machine Translation paradigm. This implementation is done in Python and Chainer, an easy-to-use Deep Learning Framework.
Compositional Distributional Models of Meaning (2016)

Compositional distributional models of meaning (CDMs) provide a function that produces a vectorial representation for a phrase or a sentence by composing the vectors of its words. Being the natural evolution of the traditional and well-studied distributional models at the word level, CDMs are steadily evolving to a popular and active area of NLP. This COLING 2016 tutorial aims at providing a concise introduction to this emerging field, presenting the different classes of CDMs and the various issues related to them in sufficient detail.
Natural Language Processing for Intelligent Access to Scientific Information (2016)

During the last decade the amount of scientific information available on-line increased at an unprecedented rate. As a consequence, nowadays researchers are overwhelmed by an enormous and continuously growing number of articles to consider when they perform research activities like the exploration of advances in specific topics, peer reviewing, writing and evaluation of proposals. Natural Language Processing Technology represents a key enabling factor in providing scientists with intelligent patterns to access to scientific information. Extracting information from scientific papers, for example, can contribute to the development of rich scientific knowledge bases which can be leveraged to support intelligent knowledge access and question answering. Summarization techniques can reduce the size of long papers to their essential content or automatically generate state-of-the-art-reviews. Paraphrase or textual entailment techniques can contribute to the identification of relations across different scientific textual sources. This tutorial provides an overview of the most relevant tasks related to the processing of scientific documents, including but not limited to the in-depth analysis of the structure of the scientific articles, their semantic interpretation, content extraction and summarization.
Quality Estimation for Language Output Applications (2016)

Quality Estimation (QE) of language output applications is a research area that has been attracting significant attention. The goal of QE is to estimate the quality of language output applications without the need of human references. Instead, machine learning algorithms are used to build supervised models based on a few labelled training instances. Such models are able to generalise over unseen data and thus QE is a robust method applicable to scenarios where human input is not available or possible. One such a scenario where QE is particularly appealing is that of Machine Translation, where a score for predicted quality can help decide whether or not a translation is useful (e.g. for post-editing) or reliable (e.g. for gisting). Other potential applications within Natural Language Processing (NLP) include Text Summarisation and Text Simplification. In this tutorial we present the task of QE and its application in NLP, focusing on Machine Translation. We also introduce QuEst++, a toolkit for QE that encompasses feature extraction and machine learning, and propose a practical activity to extend this toolkit in various ways.
Translationese: Between Human and Machine Translation (2016)

Translated texts, in any language, have unique characteristics that set them apart from texts originally written in the same language. Translation Studies is a research field that focuses on investigating these characteristics. Until recently, research in machine translation (MT) has been entirely divorced from translation studies. The main goal of this tutorial is to introduce some of the findings of translation studies to researchers interested mainly in machine translation, and to demonstrate that awareness to these findings can result in better, more accurate MT systems.
Succinct Data Structures for NLP-at-Scale (2016)

Succinct data structures involve the use of novel data structures, compression technologies, and other mechanisms to allow data to be stored in extremely small memory or disk footprints, while still allowing for efficient access to the underlying data. They have successfully been applied in areas such as Information Retrieval and Bioinformatics to create highly compressible in-memory search indexes which provide efficient search functionality over datasets which traditionally could only be processed using external memory data structures. Modern technologies in this space are not well known within the NLP community, but have the potential to revolutionise NLP, particularly the application to {\textquoteleft}big data' in the form of terabyte and larger corpora. This tutorial will present a practical introduction to the most important succinct data structures, tools, and applications with the intent of providing the researchers with a jump-start into this domain. The focus of this tutorial will be efficient text processing utilising space efficient representations of suffix arrays, suffix trees and searchable integer compression schemes with specific applications of succinct data structures to common NLP tasks such as $n$-gram language modelling.
The Role of Wikipedia in Text Analysis and Retrieval (2016)

This tutorial examines the characteristics, advantages and limitations of Wikipedia relative to other existing, human-curated resources of knowledge; derivative resources, created by converting semi-structured content in Wikipedia into structured data; the role of Wikipedia and its derivatives in text analysis; and the role of Wikipedia and its derivatives in enhancing information retrieval.
Le VOT des \'ejectives : le cas du maya yucat\`eque (The VOT of ejective stops in Maya Yucatec) (2016)

Cet article pr{\'e}sente une {\'e}tude acoustique des occlusives {\'e}jectives du maya yucat{\`e}que. S`int{\'e}ressant sp{\'e}cifiquement au voice onset time (VOT), l'{\'e}tude examine d`une part si le VOT est un corr{\'e}lat acoustique fiable de l'{\'e}jectivit{\'e} dans cette langue et d`autre part si le VOT varie selon le lieu d`articulation et la hauteur vocalique. Les r{\'e}sultats, obtenus {\`a} partir des productions de deux locuteurs natifs, montrent que les {\'e}jectives ont un VOT plus long compar{\'e}es {\`a} leurs contreparties pulmonaires. Parmi les {\'e}jectives, le VOT varie en fonction du lieu d`articulation, les v{\'e}laires pr{\'e}sentant le VOT le plus long. De m{\^e}me une tendance pour un VOT plus court devant les voyelles hautes a {\'e}t{\'e} observ{\'e}e. Ces r{\'e}sultats soul{\`e}vent un ensemble de questions concernant les m{\'e}canismes qui sous-tendent les variations du VOT, notamment en lien avec les contraintes a{\'e}rodynamiques en jeu lors de la production des occlusives {\'e}jectives.
Accommodation temporelle chez l`enfant dans une t\^ache de parole altern\'ee (Children`s temporal accommodation in an alternated naming task) (2016)

L`accommodation temporelle entre deux interlocuteurs est un ph{\'e}nom{\`e}ne qui {\'e}merge lors d`une interaction et qui jouerait un r{\^o}le important dans la fluidit{\'e} des {\'e}changes. Cette {\'e}tude examine cette capacit{\'e} temporelle chez l`enfant {\^a}g{\'e} de 5 {\`a} 6 ans gr{\^a}ce au d{\'e}veloppement d`une nouvelle t{\^a}che de d{\'e}nomination en alternance avec un partenaire virtuel. Les variables temporelles analys{\'e}es sont le tempo de l`alternance (lent versus rapide) et la rythmicit{\'e} des mots {\'e}chang{\'e}s (constante versus al{\'e}atoire). Les enfants sont plus pr{\'e}cis dans la condition de tempo rapide et plus r{\'e}guliers lorsque la rythmicit{\'e} des listes de mots est maintenue constante. Ces r{\'e}sultats montrent 1) que la d{\'e}nomination en alternance est un paradigme permettant de mesurer les capacit{\'e}s d`accommodation temporelle des enfants et que 2) d{\`e}s 5 ans, les enfants peuvent ajuster leur parole {\`a} celle d`un agent. Ces donn{\'e}es constituent une base pour mesurer les capacit{\'e}s linguistiques d`accommodation temporelle chez des populations cliniques.
Acc\`es lexical et reconnaissance du voisement en voix chuchot\'ee (Lexical acces and recognition of voicing in whisper) (2016)

La reconnaissance du trait de voisement de consonnes obstruantes chuchot{\'e}es en fran{\c{c}}ais a {\'e}t{\'e} examin{\'e}e via un paradigme d`amor{\c{c}}age s{\'e}mantique auditif-visuel. Un effet d`amor{\c{c}}age d`amplitude similaire {\`a} celui mesur{\'e} en voix modale a {\'e}t{\'e} observ{\'e} uniquement lorsque l`obstruante du mot amorce chuchot{\'e} est sourde (dessert-CHOCOLAT). Aucun effet d`amor{\c{c}}age n`a {\'e}t{\'e} not{\'e} quand l`obstruante du mot amorce est vois{\'e}e (d{\'e}sert) que ce soit sur le mot cible SABLE associ{\'e} s{\'e}mantique de d{\'e}sert ou sur le mot cible CHOCOLAT associ{\'e} s{\'e}mantique de dessert. Ainsi, m{\^e}me si certaines travaux ont mis en {\'e}vidence qu`en voix chuchot{\'e}e les consonnes obstruantes vois{\'e}es maintiennent des traces phon{\'e}tiques de leur identit{\'e} sous-jacente, notre {\'e}tude montre que ces consonnes sont ambig{\"u}es pour l`auditeur et que leur reconnaissance n`est pas imm{\'e}diate.
Acquisition et reconnaissance automatique d`expressions et d`appels vocaux dans un habitat. (Acquisition and recognition of expressions and vocal calls in a smart home) (2016)

Cet article pr{\'e}sente un syst{\`e}me capable de reconna{\^i}tre les appels {\`a} l`aide de personnes {\^a}g{\'e}es vivant {\`a} domicile afin de leur fournir une assistance. Le syst{\`e}me utilise une technologie de Reconnaissance Automatique de la Parole (RAP) qui doit fonctionner en conditions de parole distante et avec de la parole expressive. Pour garantir l`intimit{\'e}, le syst{\`e}me s`ex{\'e}cute localement et ne reconna{\^i}t que des phrases pr{\'e}d{\'e}finies. Le syst{\`e}me a {\'e}t{\'e} {\'e}valu{\'e} par 17 participants jouant des sc{\'e}narios incluant des chutes dans un Living lab reproduisant un salon. Le taux d`erreur de d{\'e}tection obtenu, 29{\%}, est encourageant et souligne les d{\'e}fis {\`a} surmonter pour cette t{\^a}che.
Adaptation de la prononciation pour la synth\`ese de la parole spontan\'ee en utilisant des informations linguistiques (Pronunciation adaptation for spontaneous speech synthesis using linguistic information) (2016)

Cet article pr{\'e}sente une nouvelle m{\'e}thode d`adaptation de la prononciation dont le but est de reproduire le style spontan{\'e}. Il s`agit d`une t{\^a}che-cl{\'e} en synth{\`e}se de la parole car elle permet d`apporter de l`expressivit{\'e} aux signaux produits, ouvrant ainsi la voie {\`a} de nouvelles applications. La force de la m{\'e}thode propos{\'e}e est de ne s`appuyer que sur des informations linguistiques et de consid{\'e}rer un cadre probabiliste pour ce faire, pr{\'e}cis{\'e}ment les champs al{\'e}atoires conditionnels. Dans cet article, nous {\'e}tudions tout d`abord la pertinence d`un ensemble d`informations pour l`adaptation, puis nous combinons les informations les plus pertinentes lors d`exp{\'e}riences finales. Les {\'e}valuations de la m{\'e}thode sur un corpus de parole conversationnelle en anglais montrent que les prononciations adapt{\'e}es refl{\`e}tent significativement mieux un style spontan{\'e} que les prononciations canoniques.
Alignement de s\'equences phon\'etiques pour une analyse phonologique des erreurs de transcription automatique (Phonetic sequences alignment for a phonemic analysis of automatic speech transcription errors ) (2016)

La transcription automatique de la parole obtient aujourd`hui des performances {\'e}lev{\'e}es avec des taux d`erreur qui tombent facilement en dessous de 10{\%} pour une parole journalistique. Cependant, pour des conversations plus libres, ils stagnent souvent autour de 20{--}30{\%}. En fran{\c{c}}ais, une grande partie des erreurs sont dues {\`a} des confusions entre homophones n`impliquant pas les niveaux acousticophon{\'e}tique et phonologique. Cependant, de nombreuses erreurs peuvent s`expliquer par des variantes de productions non pr{\'e}vues par le syst{\`e}me. Afin de mieux comprendre quels processus phonologiques pourraient expliquer ces variantes sp{\'e}cifiques de la parole spontan{\'e}e, nous proposons une analyse des erreurs en comparant prononciations attendue (r{\'e}f{\'e}rence) et reconnue (hypoth{\`e}se) via un alignement phon{\'e}tique par programmation dynamique. Les distances locales entre paires de phon{\`e}mes appari{\'e}s correspondent au nombre de traits phon{\'e}tiques disjoints. Nos analyses permettent d`identifier les traits phon{\'e}tiques les plus fr{\'e}quemment impliqu{\'e}s dans les erreurs et donnent des pistes pour des interpr{\'e}tations phonologiques.