pwc-archive/papers-with-abstracts huggingface dataset:
Japanese conversation corpus for training and evaluation of backchannel prediction model.
Authors: Hiroaki Noguchi, Yasuhiro Katagiri, Yasuharu Den
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Spoken Dialogue Systems

In this paper, we propose an experimental method for building a specialized corpus for training and evaluating backchannel prediction models of spoken dialogue. To develop a backchannel prediction model using a machine learning technique, it is necessary to discriminate between the timings of the interlocutor Â’s speech when more listeners commonly respond with backchannels and the timings when fewer listeners do so. The proposed corpus indicates the normative timings for backchannels in each speech with millisecond accuracy. In the proposed method, we first extracted each speech comprising a single turn from recorded conversation. Second, we presented these speeches as stimuli to 89 participants and asked them to respond by key hitting whenever they thought it appropriate to respond with a backchannel. In this way, we collected 28983 responses. Third, we applied the Gaussian mixture model to the temporal distribution of the responses and estimated the center of Gaussian distribution, that is, the backchannel relevance place (BRP), in each case. Finally, we synthesized 10 pairs of stereo speech stimuli and asked 19 participants to rate each on a 7-point scale of naturalness. The results show that backchannels inserted at BRPs were significantly higher than those in the original condition.
Aix Map Task corpus: The French multimodal corpus of task-oriented dialogue
Authors: Jan Gorisch, Corine Ast{\'e}sano, Ellen Gurman Bard, Brigitte Bigi, Laurent Pr{\'e}vot
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

This paper introduces the Aix Map Task corpus, a corpus of audio and video recordings of task-oriented dialogues. It was modelled after the original HCRC Map Task corpus. Lexical material was designed for the analysis of speech and prosody, as described in Ast{\'e}sano et al. (2007). The design of the lexical material, the protocol and some basic quantitative features of the existing corpus are presented. The corpus was collected under two communicative conditions, one audio-only condition and one face-to-face condition. The recordings took place in a studio and a sound attenuated booth respectively, with head-set microphones (and in the face-to-face condition with two video cameras). The recordings have been segmented into Inter-Pausal-Units and transcribed using transcription conventions containing actual productions and canonical forms of what was said. It is made publicly available online.
Adapting a part-of-speech tagset to non-standard text: The case of STTS
Authors: Heike Zinsmeister, Ulrich Heid, Kathrin Beck
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Domain Adaptation, Machine Translation, Opinion Mining, Part-Of-Speech Tagging, Semantic Role Labeling, TAG

The Stuttgart-T{\"u}bingen TagSet (STTS) is a de-facto standard for the part-of-speech tagging of German texts. Since its first publication in 1995, STTS has been used in a variety of annotation projects, some of which have adapted the tagset slightly for their specific needs. Recently, the focus of many projects has shifted from the analysis of newspaper text to that of non-standard varieties such as user-generated content, historical texts, and learner language. These text types contain linguistic phenomena that are missing from or are only suboptimally covered by STTS; in a community effort, German NLP researchers have therefore proposed additions to and modifications of the tagset that will handle these phenomena more appropriately. In addition, they have discussed alternative ways of tag assignment in terms of bipartite tags (stem, token) for historical texts and tripartite tags (lexicon, morphology, distribution) for learner texts. In this article, we report on this ongoing activity, addressing methodological issues and discussing selected phenomena and their treatment in the tagset adaptation process.
Alert!... Calm Down, There is Nothing to Worry About. Warning and Soothing Speech Synthesis.
Authors: Milan Rusko, Sakhia Darjaa, Mari{\'a}n Trnka, Mari{\'a}n Ritomsk{\'y}, R{\'o}bert Sabo
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Expressive Speech Synthesis, Sentence, Speech Synthesis

Presence of appropriate acoustic cues of affective features in the synthesized speech can be a prerequisite for the proper evaluation of the semantic content by the message recipient. In the recent work the authors have focused on the research of expressive speech synthesis capable of generating naturally sounding synthetic speech at various levels of arousal. The synthesizer should be able to produce speech in Slovak in different styles from extremely urgent warnings, insisting messages, alerts, through comments, and neutral style speech to soothing messages and very calm speech. A three-step method was used for recording both - the high-activation and low-activation expressive speech databases. The acoustic properties of the obtained databases are discussed. Several synthesizers with different levels of arousal were designed using these databases and their outputs are compared to the original voice of the voice talent. A possible ambiguity of acoustic cues is pointed out and the relevance of the semantic meaning of the sentences both in the sentence set for the speech database recording and in the set for subjective synthesizer testing is discussed.
Multiword Expressions in Machine Translation
Authors: Valia Kordoni, Iliana Simova
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Language Modelling, Machine Translation, Translation, Word Translation

This work describes an experimental evaluation of the significance of phrasal verb treatment for obtaining better quality statistical machine translation (SMT) results. The importance of the detection and special treatment of phrasal verbs is measured in the context of SMT, where the word-for-word translation of these units often produces incoherent results. Two ways of integrating phrasal verb information in a phrase-based SMT system are presented. Automatic and manual evaluations of the results reveal improvements in the translation quality in both experiments.
CROMER: a Tool for Cross-Document Event and Entity Coreference
Authors: Christian Girardi, Manuela Speranza, Rachele Sprugnoli, Sara Tonelli
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Coreference Resolution, Retrieval

In this paper we present CROMER (CROss-document Main Events and entities Recognition), a novel tool to manually annotate event and entity coreference across clusters of documents. The tool has been developed so as to handle large collections of documents, perform collaborative annotation (several annotators can work on the same clusters), and enable the linking of the annotated data to external knowledge sources. Given the availability of semantic information encoded in Semantic Web resources, this tool is designed to support annotators in linking entities and events to DBPedia and Wikipedia, so as to facilitate the automatic retrieval of additional semantic information. In this way, event modelling and chaining is made easy, while guaranteeing the highest interconnection with external resources. For example, the tool can be easily linked to event models such as the Simple Event Model [Van Hage et al , 2011] and the Grounded Annotation Framework [Fokkens et al. 2013].
RSS-TOBI - A Prosodically Enhanced Romanian Speech Corpus
Authors: Tiberiu Boro{\textcommabelow{s}}, Adriana Stan, Oliver Watts, Stefan Daniel Dumitrescu
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

This paper introduces a recent development of a Romanian Speech corpus to include prosodic annotations of the speech data in the form of ToBI labels. We describe the methodology of determining the required pitch patterns that are common for the Romanian language, annotate the speech resource, and then provide a comparison of two text-to-speech synthesis systems to establish the benefits of using this type of information to our speech resource. The result is a publicly available speech dataset which can be used to further develop speech synthesis systems or to automatically learn the prediction of ToBI labels from text in Romanian language.
Coreference Resolution for Latvian
Authors: Art{\=u}rs Znoti{\c{n}}{\v{s}}, P{\=e}teris Paikens
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: coreference-resolution, Coreference Resolution, Named Entity Recognition (NER), NER, Part-Of-Speech Tagging, Question Answering, Text Summarization

Coreference resolution (CR) is a current problem in natural language processing (NLP) research and it is a key task in applications such as question answering, text summarization and information extraction for which text understanding is of crucial importance. We describe an implementation of coreference resolution tools for Latvian language, developed as a part of a tool chain for newswire text analysis but usable also as a separate, publicly available module. LVCoref is a rule based CR system that uses entity centric model that encourages the sharing of information across all mentions that point to the same real-world entity. The system is developed to provide starting ground for further experiments and generate a reference baseline to be compared with more advanced rule-based and machine learning based future coreference resolvers. It now reaches 66.6 F-score using predicted mentions and 78.1{\%} F-score using gold mentions. This paper describes current efforts to create a CR system and to improve NER performance for Latvian. Task also includes creation of the corpus of manually annotated coreference relations.
How Could Veins Speed Up The Process Of Discourse Parsing
Authors: Elena Mitocariu, Daniel Anechitei, Dan Cristea
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Discourse Parsing

In this paper we propose a method of reducing the search space of a discourse parsing process, while keeping unaffected its capacity to generate cohesive and coherent tree structures. The parsing method uses Veins Theory (VT), by developing incrementally a forest of parallel discourse trees, evaluating them on cohesion and coherence criteria and keeping only the most promising structures to go on with at each step. The incremental development is constrained by two general principles, well known in discourse parsing: sequentiality of the terminal nodes and attachment restricted to the right frontier. A set of formulas rooted on VT helps to guess the most promising nodes of the right frontier where an attachment can be made, thus avoiding an exhaustive generation of the whole search space and in the same time maximizing the coherence of the discourse structures. We report good results of applying this approach, representing a significant improvement in discourse parsing process.
How to construct a multi-lingual domain ontology
Authors: Nitsan Chrizman, Alon Itai
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Articles, Relational Reasoning

The research focuses on automatic construction of multi-lingual domain-ontologies, i.e., creating a DAG (directed acyclic graph) consisting of concepts relating to a specific domain and the relations between them. The domain example on which the research performed is {``}Organized Crime{''}. The contribution of the work is the investigation of and comparison between several data sources and methods to create multi-lingual ontologies. The first subtask was to extract the domain{'}s concepts. The best source turned out to be WikepediaÂ’s articles that are under the catgegory. The second task was to create an English ontology, i.e., the relationships between the concepts. Again the relationships between concepts and the hierarchy were derived from Wikipedia. The final task was to create an ontology for a language with far fewer resources (Hebrew). The task was accomplished by deriving the concepts from the Hebrew Wikepedia and assessing their relevance and the relationships between them from the English ontology.
Automatic language identity tagging on word and sentence-level in multilingual text sources: a case-study on Luxembourgish
Authors: Thomas Lavergne, Gilles Adda, Martine Adda-Decker, Lori Lamel
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Identification, Language Modeling, Language Modelling, Sentence, speech-recognition, Speech Recognition

Luxembourgish, embedded in a multilingual context on the divide between Romance and Germanic cultures, remains one of Europe{'}s under-described languages. This is due to the fact that the written production remains relatively low, and linguistic knowledge and resources, such as lexica and pronunciation dictionaries, are sparse. The speakers or writers will frequently switch between Luxembourgish, German, and French, on a per-sentence basis, as well as on a sub-sentence level. In order to build resources like lexicons, and especially pronunciation lexicons, or language models needed for natural language processing tasks such as automatic speech recognition, language used in text corpora should be identified. In this paper, we present the design of a manually annotated corpus of mixed language sentences as well as the tools used to select these sentences. This corpus of difficult sentences was used to test a word-based language identification system. This language identification system was used to select textual data extracted from the web, in order to build a lexicon and language models. This lexicon and language model were used in an Automatic Speech Recognition system for the Luxembourgish language which obtain a 25{\textbackslash}{\%} WER on the Quaero development data.
Towards Shared Datasets for Normalization Research
Authors: Orph{\'e}e De Clercq, Sarah Schulz, Bart Desmet, V{\'e}ronique Hoste
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Domain Adaptation, Lexical Normalization, Machine Translation, Opinion Mining, Text Normalization, Translation

In this paper we present a Dutch and English dataset that can serve as a gold standard for evaluating text normalization approaches. With the combination of text messages, message board posts and tweets, these datasets represent a variety of user generated content. All data was manually normalized to their standard form using newly-developed guidelines. We perform automatic lexical normalization experiments on these datasets using statistical machine translation techniques. We focus on both the word and character level and find that we can improve the BLEU score with ca. 20{\%} for both languages. In order for this user generated content data to be released publicly to the research community some issues first need to be resolved. These are discussed in closer detail by focussing on the current legislation and by investigating previous similar data collection projects. With this discussion we hope to shed some light on various difficulties researchers are facing when trying to share social media data.
Rule-based Reordering Space in Statistical Machine Translation
Authors: Nicolas P{\'e}cheux, Alex Allauzen, er, Fran{\c{c}}ois Yvon
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Translation

In Statistical Machine Translation (SMT), the constraints on word reorderings have a great impact on the set of potential translations that are explored. Notwithstanding computationnal issues, the reordering space of a SMT system needs to be designed with great care: if a larger search space is likely to yield better translations, it may also lead to more decoding errors, because of the added ambiguity and the interaction with the pruning strategy. In this paper, we study this trade-off using a state-of-the art translation system, where all reorderings are represented in a word lattice prior to decoding. This allows us to directly explore and compare different reordering spaces. We study in detail a rule-based preordering system, varying the length or number of rules, the tagset used, as well as contrasting with oracle settings and purely combinatorial subsets of permutations. We focus on two language pairs: English-French, a close language pair and English-German, known to be a more challenging reordering pair.
KALAKA-3: a database for the recognition of spoken European languages on YouTube audios
Authors: Luis Javier Rodr{\'\i}guez-Fuentes, Mikel Penagarikano, Amparo Varona, Mireia Diez, Germ{\'a}n Bordel
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

This paper describes the main features of KALAKA-3, a speech database specifically designed for the development and evaluation of language recognition systems. The database provides TV broadcast speech for training, and audio data extracted from YouTube videos for tuning and testing. The database was created to support the Albayzin 2012 Language Recognition Evaluation, which featured two language recognition tasks, both dealing with European languages. The first one involved six target languages (Basque, Catalan, English, Galician, Portuguese and Spanish) for which there was plenty of training data, whereas the second one involved four target languages (French, German, Greek and Italian) for which no training data was provided. Two separate sets of YouTube audio files were provided to test the performance of language recognition systems on both tasks. To allow open-set tests, these datasets included speech in 11 additional (Out-Of-Set) European languages. The paper also presents a summary of the results attained in the evaluation, along with the performance of state-of-the-art systems on the four evaluation tracks defined on the database, which demonstrates the extreme difficulty of some of them. As far as we know, this is the first database specifically designed to benchmark spoken language recognition technology on YouTube audios.
Mining Online Discussion Forums for Metaphors
Authors: Andrew Gargett, John Barnden
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

We present an approach to mining online forums for figurative language such as metaphor. We target in particular online discussions within the illness and the political conflict domains, with a view to constructing corpora of Metaphor in Illness Discussion, andMetaphor in Political Conflict Discussion. This paper reports on our ongoing efforts to combine manual and automatic detection strategies for labelling the corpora, and present some initial results from our work showing that metaphor use is not independent of illness domain.
TagNText: A parallel corpus for the induction of resource-specific non-taxonomical relations from tagged images
Authors: Theodosia Togia, Ann Copestake
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Relation Extraction, TAG

When producing textual descriptions, humans express propositions regarding an object; but what do they express when annotating a document with simple tags? To answer this question, we have studied what users of tagging systems would have said if they were to describe a resource with fully fledged text. In particular, our work attempts to answer the following questions: if users were to use full descriptions, would their current tags be words present in these hypothetical sentences? If yes, what kind of language would connect these words? Such questions, although central to the problem of extracting binary relations between tags, have been sidestepped in the existing literature, which has focused on a small subset of possible inter-tag relations, namely hierarchical ones (e.g. {``}car{''} --is-a-- {``}vehicle{''}), as opposed to non-taxonomical relations (e.g. {``}woman{''} --wears-- {``}hat{''}). TagNText is the first attempt to construct a parallel corpus of tags and textual descriptions with respect to particular resources. The corpus provides enough data for the researcher to gain an insight into the nature of underlying relations, as well as the tools and methodology for constructing larger-scale parallel corpora that can aid non-taxonomical relation extraction.
CORILGA: a Galician Multilevel Annotated Speech Corpus for Linguistic Analysis
Authors: Carmen Garc{\'\i}a-Mateo, Antonio Cardenal, Xos{\'e} Luis Regueira, Elisa Fern{\'a}ndez Rei, Marta Martinez, Roberto Seara, Roc{\'\i}o Varela, Noem{\'\i} Basanta
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

This paper describes the CORILGA (Corpus Oral Informatizado da Lingua Galega). CORILGA is a large high-quality corpus of spoken Galician from the 1960s up to present-day, including both formal and informal spoken language from both standard and non-standard varieties, and across different generations and social levels. The corpus will be available to the research community upon completion. Galician is one of the EU languages that needs further research before highly effective language technology solutions can be implemented. A software repository for speech resources in Galician is also described. The repository includes a structured database, a graphical interface and processing tools. The use of a database enables to perform search in a simple and fast way based in a number of different criteria. The web-based user interface facilitates users the access to the different materials. Last but not least a set of transcription-based modules for automatic speech recognition has been developed, thus facilitating the orthographic labelling of the recordings.
Overview of Todai Robot Project and Evaluation Framework of its NLP-based Problem Solving
Authors: Akira Fujita, Akihiro Kameda, Ai Kawazoe, Yusuke Miyao
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Benchmarking

We introduce the organization of the Todai Robot Project and discuss its achievements. The Todai Robot Project task focuses on benchmarking NLP systems for problem solving. This task encourages NLP-based systems to solve real high-school examinations. We describe the details of the method to manage question resources and their correct answers, answering tools and participation by researchers in the task. We also analyse the answering accuracy of the developed systems by comparing the systemsÂ’ answers with answers given by human test-takers.
A Database of Full Body Virtual Interactions Annotated with Expressivity Scores
Authors: Demulier Virginie, Elisabetta Bevacqua, Florian Focone, Tom Giraud, Pamela Carreno, Brice Isableu, Sylvie Gibet, Pierre De Loor, Jean-Claude Martin
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

Recent technologies enable the exploitation of full body expressions in applications such as interactive arts but are still limited in terms of dyadic subtle interaction patterns. Our project aims at full body expressive interactions between a user and an autonomous virtual agent. The currently available databases do not contain full body expressivity and interaction patterns via avatars. In this paper, we describe a protocol defined to collect a database to study expressive full-body dyadic interactions. We detail the coding scheme for manually annotating the collected videos. Reliability measures for global annotations of expressivity and interaction are also provided.