pwc-archive/papers-with-abstracts huggingface dataset:
Manual Analysis of Structurally Informed Reordering in German-English Machine Translation
Authors: Teresa Herrmann, Jan Niehues, Alex Waibel
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Sentence, Translation

Word reordering is a difficult task for translation. Common automatic metrics such as BLEU have problems reflecting improvements in target language word order. However, it is a crucial aspect for humans when deciding on translation quality. This paper presents a detailed analysis of a structure-aware reordering approach applied in a German-to-English phrase-based machine translation system. We compare the translation outputs of two translation systems applying reordering rules based on parts-of-speech and syntax trees on a sentence-by-sentence basis. For each sentence-pair we examine the global translation performance and classify local changes in the translated sentences. This analysis is applied to three data sets representing different genres. While the improvement in BLEU differed substantially between the data sets, the manual evaluation showed that both global translation performance as well as individual types of improvements and degradations exhibit a similar behavior throughout the three data sets. We have observed that for 55-64{\%} of the sentences with different translations, the translation produced using the tree-based reordering was considered to be the better translation. As intended by the investigated reordering model, most improvements are achieved by improving the position of the verb or being able to translate a verb that could not be translated before.
Presenting a system of human-machine interaction for performing map tasks.
Authors: Gabriele Pallotti, Francesca Frontini, Fabio Aff{\`e}, Monica Monachini, Stefania Ferrari
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Language Acquisition

A system for human machine interaction is presented, that offers second language learners of Italian the possibility of assessing their competence by performing a map task, namely by guiding the a virtual follower through a map with written instructions in natural language. The underlying natural language processing algorithm is described, and the map authoring infrastructure is presented.
Automatic Extraction of Synonyms for German Particle Verbs from Parallel Data with Distributional Similarity as a Re-Ranking Feature
Authors: Moritz Wittmann, Marion Weller, Sabine Schulte im Walde
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Language Modeling, Language Modelling, Lemmatization, Machine Translation, Part-Of-Speech Tagging, Re-Ranking, Translation

We present a method for the extraction of synonyms for German particle verbs based on a word-aligned German-English parallel corpus: by translating the particle verb to a pivot, which is then translated back, a set of synonym candidates can be extracted and ranked according to the respective translation probabilities. In order to deal with separated particle verbs, we apply re-ordering rules to the German part of the data. In our evaluation against a gold standard, we compare different pre-processing strategies (lemmatized vs. inflected forms) and introduce language model scores of synonym candidates in the context of the input particle verb as well as distributional similarity as additional re-ranking criteria. Our evaluation shows that distributional similarity as a re-ranking feature is more robust than language model scores and leads to an improved ranking of the synonym candidates. In addition to evaluating against a gold standard, we also present a small-scale manual evaluation.
NASTIA: Negotiating Appointment Setting Interface
Authors: Layla El Asri, R{\'e}mi Lemonnier, Romain Laroche, Olivier Pietquin, Hatim Khouzaimi
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Decision Making, Scheduling, slot-filling, Slot Filling, Spoken Dialogue Systems

This paper describes a French Spoken Dialogue System (SDS) named NASTIA (Negotiating Appointment SeTting InterfAce). Appointment scheduling is a hybrid task halfway between slot-filling and negotiation. NASTIA implements three different negotiation strategies. These strategies were tested on 1734 dialogues with 385 users who interacted at most 5 times with the SDS and gave a rating on a scale of 1 to 10 for each dialogue. Previous appointment scheduling systems were evaluated with the same experimental protocol. NASTIA is different from these systems in that it can adapt its strategy during the dialogue. The highest system task completion rate with these systems was 81{\%} whereas NASTIA had an 88{\%} average and its best performing strategy even reached 92{\%}. This strategy also significantly outperformed previous systems in terms of overall user rating with an average of 8.28 against 7.40. The experiment also enabled highlighting global recommendations for building spoken dialogue systems.
DINASTI: Dialogues with a Negotiating Appointment Setting Interface
Authors: Layla El Asri, Romain Laroche, Olivier Pietquin
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Dialogue Management, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Speech Recognition, Spoken Dialogue Systems

This paper describes the DINASTI (DIalogues with a Negotiating Appointment SeTting Interface) corpus, which is composed of 1734 dialogues with the French spoken dialogue system NASTIA (Negotiating Appointment SeTting InterfAce). NASTIA is a reinforcement learning-based system. The DINASTI corpus was collected while the system was following a uniform policy. Each entry of the corpus is a system-user exchange annotated with 120 automatically computable features.The corpus contains a total of 21587 entries, with 385 testers. Each tester performed at most five scenario-based interactions with NASTIA. The dialogues last an average of 10.82 dialogue turns, with 4.45 reinforcement learning decisions. The testers filled an evaluation questionnaire after each dialogue. The questionnaire includes three questions to measure task completion. In addition, it comprises 7 Likert-scaled items evaluating several aspects of the interaction, a numerical overall evaluation on a scale of 1 to 10, and a free text entry. Answers to this questionnaire are provided with DINASTI. This corpus is meant for research on reinforcement learning modelling for dialogue management.
LQVSumm: A Corpus of Linguistic Quality Violations in Multi-Document Summarization
Authors: Annemarie Friedrich, Marina Valeeva, Alexis Palmer
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Document Summarization, Multi-Document Summarization, Sentence Compression

We present LQVSumm, a corpus of about 2000 automatically created extractive multi-document summaries from the TAC 2011 shared task on Guided Summarization, which we annotated with several types of linguistic quality violations. Examples for such violations include pronouns that lack antecedents or ungrammatical clauses. We give details on the annotation scheme and show that inter-annotator agreement is good given the open-ended nature of the task. The annotated summaries have previously been scored for Readability on a numeric scale by human annotators in the context of the TAC challenge; we show that the number of instances of violations of linguistic quality of a summary correlates with these intuitively assigned numeric scores. On a system-level, the average number of violations marked in a system{'}s summaries achieves higher correlation with the Readability scores than current supervised state-of-the-art methods for assigning a single readability score to a summary. It is our hope that our corpus facilitates the development of methods that not only judge the linguistic quality of automatically generated summaries as a whole, but which also allow for detecting, labeling, and fixing particular violations in a text.
Potsdam Commentary Corpus 2.0: Annotation for Discourse Research
Authors: Manfred Stede, Arne Neumann
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

We present a revised and extended version of the Potsdam Commentary Corpus, a collection of 175 German newspaper commentaries (op-ed pieces) that has been annotated with syntax trees and three layers of discourse-level information: nominal coreference,connectives and their arguments (similar to the PDTB, Prasad et al. 2008), and trees reflecting discourse structure according to Rhetorical Structure Theory (Mann/Thompson 1988). Connectives have been annotated with the help of a semi-automatic tool, Conano (Stede/Heintze 2004), which identifies most connectives and suggests arguments based on their syntactic category. The other layers have been created manually with dedicated annotation tools. The corpus is made available on the one hand as a set of original XML files produced with the annotation tools, based on identical tokenization. On the other hand, it is distributed together with the open-source linguistic database ANNIS3 (Chiarcos et al. 2008; Zeldes et al. 2009), which provides multi-layer search functionality and layer-specific visualization modules. This allows for comfortable qualitative evaluation of the correlations between annotation layers.
GL\`AFF, a Large Versatile French Lexicon
Authors: Nabil Hathout, Franck Sajous, Basilio Calderone
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

This paper introduces GLAFF, a large-scale versatile French lexicon extracted from Wiktionary, the collaborative online dictionary. GLAFF contains, for each entry, inflectional features and phonemic transcriptions. It distinguishes itself from the other available French lexicons by its size, its potential for constant updating and its copylefted license. We explain how we have built GLAFF and compare it to other known resources in terms of coverage and quality of the phonemic transcriptions. We show that its size and quality are strong assets that could allow GLAFF to become a reference lexicon for French NLP and linguistics. Moreover, other derived lexicons can easily be based on GLAFF to satisfy specific needs of various fields such as psycholinguistics.
Dense Components in the Structure of WordNet
Authors: Ahti Lohk, Kaarel Allik, Heili Orav, V{\~o}h, Leo u
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Information Retrieval, Machine Translation, Text Categorization, Word Sense Disambiguation

This paper introduces a test-pattern named a dense component for checking inconsistencies in the hierarchical structure of a wordnet. Dense component (viewed as substructure) points out the cases of regular polysemy in the context of multiple inheritance. Definition of the regular polysemy is redefined â€• instead of lexical units there are used lexical concepts (synsets). All dense components are evaluated by expert lexicographer. Based on this experiment we give an overview of the inconsistencies which the test-pattern helps to detect. Special attention is turned to all different kind of corrections made by lexicographer. Authors of this paper find that the greatest benefit of the use of dense components is helping to detect if the regular polysemy is justified or not. In-depth analysis has been performed for Estonian Wordnet Version 66. Some comparative figures are also given for the Estonian Wordnet (EstWN) Version 67 and Princeton WordNet (PrWN) Version 3.1. Analysing hierarchies only hypernym-relations are used.
Choosing which to use? A study of distributional models for nominal lexical semantic classification
Authors: Lauren Romeo, Gianluca Lebani, N{\'u}ria Bel, Aless Lenci, ro
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: General Classification, Machine Translation, Question Answering

This paper empirically evaluates the performances of different state-of-the-art distributional models in a nominal lexical semantic classification task. We consider models that exploit various types of distributional features, which thereby provide different representations of nominal behavior in context. The experiments presented in this work demonstrate the advantages and disadvantages of each model considered. This analysis also considers a combined strategy that we found to be capable of leveraging the bottlenecks of each model, especially when large robust data is not available.
Extensions of the Sign Language Recognition and Translation Corpus RWTH-PHOENIX-Weather
Authors: Jens Forster, Christoph Schmidt, Oscar Koller, Martin Bellgardt, Hermann Ney
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: 2k, Object Tracking, Sentence, Sign Language Recognition, speech-recognition, Speech Recognition, Translation

This paper introduces the RWTH-PHOENIX-Weather 2014, a video-based, large vocabulary, German sign language corpus which has been extended over the last two years, tripling the size of the original corpus. The corpus contains weather forecasts simultaneously interpreted into sign language which were recorded from German public TV and manually annotated using glosses on the sentence level and semi-automatically transcribed spoken German extracted from the videos using the open-source speech recognition system RASR. Spatial annotations of the signers{'} hands as well as shape and orientation annotations of the dominant hand have been added for more than 40k respectively 10k video frames creating one of the largest corpora allowing for quantitative evaluation of object tracking algorithms. Further, over 2k signs have been annotated using the SignWriting annotation system, focusing on the shape, orientation, movement as well as spatial contacts of both hands. Finally, extended recognition and translation setups are defined, and baseline results are presented.
HESITA(te) in Portuguese
Authors: C, Sara eias, Dirce Celorico, Jorge Proen{\c{c}}a, Arlindo Veiga, Carla Lopes, Fern Perdig{\~a}o, o
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Acoustic Modelling, Automatic Speech Recognition, Automatic Speech Recognition (ASR), Management, speech-recognition, Speech Recognition, Speech Synthesis

Hesitations, so-called disfluencies, are a characteristic of spontaneous speech, playing a primary role in its structure, reflecting aspects of the language production and the management of inter-communication. In this paper we intend to present a database of hesitations in European Portuguese speech - HESITA - as a relevant base of work to study a variety of speech phenomena. Patterns of hesitations, hesitation distribution according to speaking style, and phonetic properties of the fillers are some of the characteristics we extrapolated from the HESITA database. This database also represents an important resource for improvement in synthetic speech naturalness as well as in robust acoustic modelling for automatic speech recognition. The HESITA database is the output of a project in the speech-processing field for European Portuguese held by an interdisciplinary group in intimate articulation between engineering tools and experience and the linguistic approach.
MUHIT: A Multilingual Harmonized Dictionary
Authors: Sameh Alansary
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Translation

This paper discusses a trial to build a multilingual harmonized dictionary that contains more than 40 languages, with special reference to Arabic which represents about 20{\%} of the whole size of the dictionary. This dictionary is called MUHIT which is an interactive multilingual dictionary application. It is a web application that makes it easily accessible to all users. MUHIT is developed within the Universal Networking Language (UNL) framework by the UNDL Foundation, in cooperation with Bibliotheca Alexandrina (BA). This application targets to serve specialists and non-specialists. It provides users with full linguistic description to each lexical item. This free application is useful to many NLP tasks such as multilingual translation and cross-language synonym search. This dictionary is built depending on WordNet and corpus based approaches, in a specially designed linguistic environment called UNLariam that is developed by the UNLD foundation. This dictionary is the first launched application by the UNLD foundation.
Predicate Matrix: extending SemLink through WordNet mappings
Authors: Maddalen Lopez de Lacalle, Egoitz Laparra, German Rigau
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Natural Language Inference, Question Answering, Semantic Parsing, Semantic Role Labeling

This paper presents the Predicate Matrix v1.1, a new lexical resource resulting from the integration of multiple sources of predicate information including FrameNet, VerbNet, PropBank and WordNet. We start from the basis of SemLink. Then, we use advanced graph-based algorithms to further extend the mapping coverage of SemLink. Second, we also exploit the current content of SemLink to infer new role mappings among the different predicate schemas. As a result, we have obtained a new version of the Predicate Matrix which largely extends the current coverage of SemLink and the previous version of the Predicate Matrix.
TaLAPi --- A Thai Linguistically Annotated Corpus for Language Processing
Authors: AiTi Aw, Sharifah Mahani Aljunied, Nattadaporn Lertcheva, Sasiwimon Kalunsima
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Articles, POS, POS Tagging, TAG

This paper discusses a Thai corpus, TaLAPi, fully annotated with word segmentation (WS), part-of-speech (POS) and named entity (NE) information with the aim to provide a high-quality and sufficiently large corpus for real-life implementation of Thai language processing tools. The corpus contains 2,720 articles (1,043,471words) from the entertainment and lifestyle (NE{\&}L) domain and 5,489 articles (3,181,487 words) in the news (NEWS) domain, with a total of 35 POS tags and 10 named entity categories. In particular, we present an approach to segment and tag foreign and loan words expressed in transliterated or original form in Thai text corpora. We see this as an area for study as adapted and un-adapted foreign language sequences have not been well addressed in the literature and this poses a challenge to the annotation process due to the increasing use and adoption of foreign words in the Thai language nowadays. To reduce the ambiguities in POS tagging and to provide rich information for facilitating Thai syntactic analysis, we adapted the POS tags used in ORCHID and propose a framework to tag Thai text and also addresses the tagging of loan and foreign words based on the proposed segmentation strategy. TaLAPi also includes a detailed guideline for tagging the 10 named entity categories
T2K\textasciicircum2: a System for Automatically Extracting and Organizing Knowledge from Texts
Authors: Felice Dell{'}Orletta, Giulia Venturi, Andrea Cimino, Simonetta Montemagni
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

In this paper, we present T2K{\textasciicircum}2, a suite of tools for automatically extracting domain―specific knowledge from collections of Italian and English texts. T2K{\textasciicircum}2 (Text―To―Knowledge v2) relies on a battery of tools for Natural Language Processing (NLP), statistical text analysis and machine learning which are dynamically integrated to provide an accurate and incremental representation of the content of vast repositories of unstructured documents. Extracted knowledge ranges from domain―specific entities and named entities to the relations connecting them and can be used for indexing document collections with respect to different information types. T2K{\textasciicircum}2 also includes linguistic profiling functionalities aimed at supporting the user in constructing the acquisition corpus, e.g. in selecting texts belonging to the same genre or characterized by the same degree of specialization or in monitoring the added value of newly inserted documents. T2K{\textasciicircum}2 is a web application which can be accessed from any browser through a personal account which has been tested in a wide range of domains.
EMOVO Corpus: an Italian Emotional Speech Database
Authors: Giovanni Costantini, Iacopo Iaderola, Andrea Paoloni, Massimiliano Todisco
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Emotion Recognition, Speech Emotion Recognition

This article describes the first emotional corpus, named EMOVO, applicable to Italian language,. It is a database built from the voices of up to 6 actors who played 14 sentences simulating 6 emotional states (disgust, fear, anger, joy, surprise, sadness) plus the neutral state. These emotions are the well-known Big Six found in most of the literature related to emotional speech. The recordings were made with professional equipment in the Fondazione Ugo Bordoni laboratories. The paper also describes a subjective validation test of the corpus, based on emotion-discrimination of two sentences carried out by two different groups of 24 listeners. The test was successful because it yielded an overall recognition accuracy of 80{\%}. It is observed that emotions less easy to recognize are joy and disgust, whereas the most easy to detect are anger, sadness and the neutral state.
MADAMIRA: A Fast, Comprehensive Tool for Morphological Analysis and Disambiguation of Arabic
Authors: Arfath Pasha, Mohamed Al-Badrashiny, Mona Diab, Ahmed El Kholy, Esk, Ramy er, Nizar Habash, Manoj Pooleery, Owen Rambow, Ryan Roth
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Chunking, Lemmatization, Machine Translation, Morphological Analysis, Morphological Tagging, Named Entity Recognition (NER), Part-Of-Speech Tagging, Transliteration

In this paper, we present MADAMIRA, a system for morphological analysis and disambiguation of Arabic that combines some of the best aspects of two previously commonly used systems for Arabic processing, MADA (Habash and Rambow, 2005; Habash et al., 2009; Habash et al., 2013) and AMIRA (Diab et al., 2007). MADAMIRA improves upon the two systems with a more streamlined Java implementation that is more robust, portable, extensible, and is faster than its ancestors by more than an order of magnitude. We also discuss an online demo (see http://nlp.ldeo.columbia.edu/madamira/) that highlights these aspects.
Developing Politeness Annotated Corpus of Hindi Blogs
Authors: Ritesh Kumar
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

In this paper I discuss the creation and annotation of a corpus of Hindi blogs. The corpus consists of a total of over 479,000 blog posts and blog comments. It is annotated with the information about the politeness level of each blog post and blog comment. The annotation is carried out using four levels of politeness ― neutral, appropriate, polite and impolite. For the annotation, three classifiers ― were trained and tested maximum entropy (MaxEnt), Support Vector Machines (SVM) and C4.5 - using around 30,000 manually annotated texts. Among these, C4.5 gave the best accuracy. It achieved an accuracy of around 78{\%} which is within 2{\%} of the human accuracy during annotation. Consequently this classifier is used to annotate the rest of the corpus