Cross-domain analysis of discourse markers in European Portuguese

This paper presents an analysis of discourse markers in two spontaneous speech corpora for European Portuguese - university lectures and map-task dialogues - and also in a collection of tweets, aiming at contributing to their categorization, scarcely existent for European Portuguese. Our results show that the selection of discourse markers is domain and speaker dependent. We also found that the most frequent discourse markers are similar in all three corpora, despite tweets containing discourse markers not found in the other two corpora. In this multidisciplinary study, comprising both a linguistic perspective and a computational approach, discourse markers are also automatically discriminated from other structural metadata events, namely sentence-like units and disfluencies. Our results show that discourse markers and disfluencies tend to co-occur in the dialogue corpus, but have a complementary distribution in the university lectures. We used three acoustic-prosodic feature sets and machine learning to automatically distinguish between discourse markers, disfluencies and sentence-like units. Our in-domain experiments achieved an accuracy of about 87% in university lectures and 84% in dialogues, in line with our previous results. The eGeMAPS features, commonly used for other paralinguistic tasks, achieved a considerable performance on our data, especially considering the small size of the feature set. Our results suggest that turn-initial discourse markers are usually easier to classify than disfluencies, a result also previously reported in the literature. We conducted a cross-domain evaluation in order to evaluate the robustness of the models across domains. The results achieved are about 11%-12% lower, but we conclude that data from one domain can still be used to classify the same events in the other. Overall, despite the complexity of this task, these are very encouraging state-of-the-art results. Ultimately, using exclusively acoustic-prosodic cues, discourse markers can be fairly discriminated from disfluencies and SUs. In order to better understand the contribution of each feature, we have also reported the impact of the features in both the dialogues and the university lectures. Pitch features are the most relevant ones for the distinction between discourse markers and disfluencies, namely pitch slopes. These features are in line with the wide pitch range of discourse markers, in a continuum from a very compressed pitch range to a very wide one, expressed by total deaccented material or H+L* L* contours, with upstep H tones

Cross-domain analysis of discourse markers in European Portuguese

This paper presents an analysis of discourse markers in two spontaneous speech corpora for European Portuguese - university lectures and map-task dialogues - and also in a collection of tweets, aiming at contributing to their categorization, scarcely existent for European Portuguese. Our results show that the selection of discourse markers is domain and speaker dependent. We also found that the most frequent discourse markers are similar in all three corpora, despite tweets containing discourse markers not found in the other two corpora. In this multidisciplinary study, comprising both a linguistic perspective and a computational approach, discourse markers are also automatically discriminated from other structural metadata events, namely sentence-like units and disfluencies. Our results show that discourse markers and disfluencies tend to co-occur in the dialogue corpus, but have a complementary distribution in the university lectures. We used three acoustic-prosodic feature sets and machine learning to automatically distinguish between discourse markers, disfluencies and sentence-like units. Our in-domain experiments achieved an accuracy of about 87% in university lectures and 84% in dialogues, in line with our previous results. The eGeMAPS features, commonly used for other paralinguistic tasks, achieved a considerable performance on our data, especially considering the small size of the feature set. Our results suggest that turn-initial discourse markers are usually easier to classify than disfluencies, a result also previously reported in the literature. We conducted a cross-domain evaluation in order to evaluate the robustness of the models across domains. The results achieved are about 11%-12% lower, but we conclude that data from one domain can still be used to classify the same events in the other. Overall, despite the complexity of this task, these are very encouraging state-of-the-art results. Ultimately, using exclusively acoustic-prosodic cues, discourse markers can be fairly discriminated from disfluencies and SUs. In order to better understand the contribution of each feature, we have also reported the impact of the features in both the dialogues and the university lectures. Pitch features are the most relevant ones for the distinction between discourse markers and disfluencies, namely pitch slopes. These features are in line with the wide pitch range of discourse markers, in a continuum from a very compressed pitch range to a very wide one, expressed by total deaccented material or H+L* L* contours, with upstep H tones.info:eu-repo/semantics/publishedVersio

Extending automatic transcripts in a unified data representation towards a prosodic-based metadata annotation and evaluation

This paper describes a framework that extends automatic speech transcripts in order to accommodate relevant information coming from manual transcripts, the speech signal itself, and other resources, like lexica. The proposed framework automatically collects, relates, computes, and stores all relevant information together in a self-contained data source, making it possible to easily provide a wide range of interconnected information suitable for speech analysis, training, and evaluating a number of automatic speech processing tasks. The main goal of this framework is to integrate different linguistic and paralinguistic layers of knowledge for a more complete view of their representation and interactions in several domains and languages. The processing chain is composed of two main stages, where the first consists of integrating the relevant manual annotations in the speech recognition data, and the second consists of further enriching the previous output in order to accommodate prosodic information. The described framework has been used for the identification and analysis of structural metadata in automatic speech transcripts. Initially put to use for automatic detection of punctuation marks and for capitalization recovery from speech data, it has also been recently used for studying the characterization of disfluencies in speech. It was already applied to several domains of Portuguese corpora, and also to English and Spanish Broadcast News corpora

An XML Coding Scheme for Multimodal Corpus Annotation

International audienceMultimodality has become one of today's most crucial challenges both for linguistics and computer science, entailing theoretical issues as well as practical ones (verbal interaction description, human-machine dialogues, virtual reality etc...). Understanding interaction processes is one of the main targets of these sciences, and requires to take into account the whole set of modalities and the way they interact.From a linguistic standpoint, language and speech analysis are based on studies of distinct research fields, such as phonetics, phonemics, syntax, semantics, pragmatics or gesture studies. Each of them have been investigated in the past either separately or in relation with another field that was considered as closely connected (e.g. syntax and semantics, prosody and syntax, etc.). The perspective adopted by modern linguistics is a considerably broader one: even though each domain reveals a certain degree of autonomy, it cannot be accounted for independently from its interactions with the other domains. Accordingly, the study of the interaction between the fields appears to be as important as the study of each distinct field. This is a pre-requisite for an elaboration of a valid theory of language. However, as important as the needs in this area might be, high level multimodal resources and adequate methods in order to construct them are scarce and unequally developed. Ongoing projects mainly focus on one modality as a main target, with an alternate modality as an optional complement. Moreover, coding standards in this field remain very partial and do not cover all the needs in terms of multimodal annotation. One of the first issues we have to face is the definition of a coding scheme providing adequate responses to the needs of the various levels encompassed, from phonetics to pragmatics or syntax. While working in the general context of international coding standards, we plan to create a specific coding standard designed to supply proper responses to the specific needs of multimodal annotation, as available solutions in the area do not seem to be totally satisfactory. <BR /

An XML Coding Scheme for Multimodal Corpus Annotation

International audienceMultimodality has become one of today's most crucial challenges both for linguistics and computer science, entailing theoretical issues as well as practical ones (verbal interaction description, human-machine dialogues, virtual reality etc...). Understanding interaction processes is one of the main targets of these sciences, and requires to take into account the whole set of modalities and the way they interact.From a linguistic standpoint, language and speech analysis are based on studies of distinct research fields, such as phonetics, phonemics, syntax, semantics, pragmatics or gesture studies. Each of them have been investigated in the past either separately or in relation with another field that was considered as closely connected (e.g. syntax and semantics, prosody and syntax, etc.). The perspective adopted by modern linguistics is a considerably broader one: even though each domain reveals a certain degree of autonomy, it cannot be accounted for independently from its interactions with the other domains. Accordingly, the study of the interaction between the fields appears to be as important as the study of each distinct field. This is a pre-requisite for an elaboration of a valid theory of language. However, as important as the needs in this area might be, high level multimodal resources and adequate methods in order to construct them are scarce and unequally developed. Ongoing projects mainly focus on one modality as a main target, with an alternate modality as an optional complement. Moreover, coding standards in this field remain very partial and do not cover all the needs in terms of multimodal annotation. One of the first issues we have to face is the definition of a coding scheme providing adequate responses to the needs of the various levels encompassed, from phonetics to pragmatics or syntax. While working in the general context of international coding standards, we plan to create a specific coding standard designed to supply proper responses to the specific needs of multimodal annotation, as available solutions in the area do not seem to be totally satisfactory. <BR /

Statistical Parsing by Machine Learning from a Classical Arabic Treebank

Research into statistical parsing for English has enjoyed over a decade of successful results. However, adapting these models to other languages has met with difficulties. Previous comparative work has shown that Modern Arabic is one of the most difficult languages to parse due to rich morphology and free word order. Classical Arabic is the ancient form of Arabic, and is understudied in computational linguistics, relative to its worldwide reach as the language of the Quran. The thesis is based on seven publications that make significant contributions to knowledge relating to annotating and parsing Classical Arabic. Classical Arabic has been studied in depth by grammarians for over a thousand years using a traditional grammar known as i’rāb (إعغاة ). Using this grammar to develop a representation for parsing is challenging, as it describes syntax using a hybrid of phrase-structure and dependency relations. This work aims to advance the state-of-the-art for hybrid parsing by introducing a formal representation for annotation and a resource for machine learning. The main contributions are the first treebank for Classical Arabic and the first statistical dependency-based parser in any language for ellipsis, dropped pronouns and hybrid representations. A central argument of this thesis is that using a hybrid representation closely aligned to traditional grammar leads to improved parsing for Arabic. To test this hypothesis, two approaches are compared. As a reference, a pure dependency parser is adapted using graph transformations, resulting in an 87.47% F1-score. This is compared to an integrated parsing model with an F1-score of 89.03%, demonstrating that joint dependency-constituency parsing is better suited to Classical Arabic. The Quran was chosen for annotation as a large body of work exists providing detailed syntactic analysis. Volunteer crowdsourcing is used for annotation in combination with expert supervision. A practical result of the annotation effort is the corpus website: http://corpus.quran.com, an educational resource with over two million users per year